Functions
DeseqObject <- function(DESIGN, countdata, coldata, consensus="None", sample_type="None", Ref) {
"
Function to create DESeq2 object
"
dds <- DESeqDataSetFromMatrix(countData = countdata,
colData = coldata,
design = as.formula(paste("~", DESIGN)))
# Kick out non-consensus samples
if (!(consensus == "None")) {
dds <- dds[, dds$paper %in% consensus]
}
# Kick out samples that are not bulk tissue
if (!(sample_type == "None")) {
dds <- dds[, dds$sample_type == sample_type]
}
dds$tissue.type <- relevel(dds$tissue.type, ref=ref)
dds$tissue.type <- droplevels(dds$tissue.type)
dds <- DESeq(dds,
parallel=TRUE,
BPPARAM=MulticoreParam(3)
)
return(dds)
}
DeseqResult <- function(dds, column, coef, tissue_type_A, tissue_type_B,
lfc.Threshold, rpm.Threshold,
norm_adj_up = "None",
norm_adj_down = "None",
pCRC_adj_up = "None",
pCRC_adj_down = "None"){
"
Function to return results from DESeq2 for different conditions,
including control for normal adjacent tissue, if available
"
p.threshold <- p.Threshold
lfc.threshold <- lfc.Threshold
rpm.threshold <- rpm.Threshold
samples_tissue_type_A <- colData(dds)[, column] == tissue_type_A
samples_tissue_type_B <- colData(dds)[, column] == tissue_type_B
res <- results(dds, name = coef, alpha = p.threshold)
res <- lfcShrink(dds, coef=coef, res=res)
rpm <- t(t(counts(dds)) / colSums(counts(dds))) * 1000000
sig <- rownames(res[(abs(res$log2FoldChange) > lfc.threshold) &
(res$padj < p.threshold) &
!is.na(res$padj), ])
sig <- sig[ (rowMeans(rpm[sig, samples_tissue_type_A]) > rpm.threshold) |
(rowMeans(rpm[sig, samples_tissue_type_B]) > rpm.threshold)
]
res_sig <- res[sig, ]
up_mirna <- rownames(res_sig[res_sig$log2FoldChange > lfc.Threshold, ])
down_mirna <- rownames(res_sig[res_sig$log2FoldChange < -lfc.Threshold, ])
if (!(norm_adj_up == "None")) {
up_mirna <- setdiff(up_mirna, norm_adj_up)
}
if (!(norm_adj_down == "None")) {
down_mirna <- setdiff(down_mirna, norm_adj_down)
}
if (!(pCRC_adj_up == "None")) {
up_mirna <- setdiff(up_mirna, pCRC_adj_up)
}
if (!(pCRC_adj_down == "None")) {
down_mirna <- setdiff(down_mirna, pCRC_adj_down)
}
return_list <- list("rpm" = rpm, "res" = res, "sig"=sig, "res_sig"=res_sig,
"down_mirna"=down_mirna, "up_mirna"=up_mirna)
return(return_list)
}
SigList <- function(res, dds, tissue_type_A, tissue_type_B, coef,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down){
"
Function to create annotated lists of signature miRNA
Return will print upregulated or downregulated miRNA,
by printing <signature_list>$up_mirna
or <signature_list>$down_mirna
"
group_A_rpm <- rowMeans(res$rpm[res$sig, dds$tissue.type == tissue_type_A])
group_A_rpm_std <- rowSds(res$rpm[res$sig, dds$tissue.type == tissue_type_A])
group_B_rpm <- rowMeans(res$rpm[res$sig, dds$tissue.type == tissue_type_B])
group_B_rpm_std <- rowSds(res$rpm[res$sig, dds$tissue.type == tissue_type_B])
lfc.deseq2 <- res$res[res$sig, ]$log2FoldChange
lfcSE.deseq2<- res$res[res$sig, ]$lfcSE
neg.log.10.adj.p <- format(res$res[res$sig, ]$padj, digits=3)
signature_mirna <- res$sig
sig_list <- dplyr::tibble(signature_mirna, lfc.deseq2, lfcSE.deseq2,
group_A_rpm, #group_A_rpm_std,
group_B_rpm, #group_B_rpm_std,
neg.log.10.adj.p)
sig_list$signature_sub <- str_replace_all(signature_mirna, "/.*", "") %>% str_replace_all(., c("_5p" = "", "_3p" = ""))
sig_list <- left_join(sig_list, MirGeneDB_info, by=c("signature_sub" = "MirGeneDB_ID"))
# create list of upregulated mirna
up_mirna <- sig_list %>%
filter(lfc.deseq2 > lfc.Threshold) %>%
# Annotate which miRNA are cell markers
mutate(
cell_marker = ifelse(signature_mirna %in% names(cell_spec_dict_inv), cell_spec_dict_inv[signature_mirna], '')) %>%
mutate(
cell_marker = cell_spec(cell_marker, color = ifelse(cell_marker != '', 'white', 'black'),
background = ifelse(cell_marker != '', 'blue', 'white'),
bold = ifelse(cell_marker != '', F, F)))
# Annotate which miRNA are in normal_adjacent
if (norm_adj_up != "None") {
up_mirna <- up_mirna %>%
mutate(
norm_adj = ifelse(signature_mirna %in% norm_adj_up, 'yes', '')) %>%
mutate(
norm_adj = cell_spec(norm_adj, color = ifelse(norm_adj == 'yes', 'white', 'black'),
background = ifelse(norm_adj == 'yes', 'black', 'white'),
bold = ifelse(norm_adj == 'yes', F, F))
)
}
else up_mirna$norm_adj <- "na"
# Annotate which miRNA are in pCRC_adjacent
if (pCRC_adj_up != "None") {
up_mirna <- up_mirna %>%
mutate(
pCRC_adj = ifelse(signature_mirna %in% pCRC_adj_up, 'yes', '')) %>%
mutate(
pCRC_adj = cell_spec(pCRC_adj, color = ifelse(pCRC_adj == 'yes', 'white', 'black'),
background = ifelse(pCRC_adj == 'yes', 'black', 'white'),
bold = ifelse(pCRC_adj == 'yes', F, F))
)
}
else up_mirna$pCRC_adj <- "na"
# number of upregulated miRNA
number_upregulated <- dim(up_mirna)[1]
# select only relevant rows
up_mirna <- up_mirna %>% select(signature_mirna, lfc.deseq2, lfcSE.deseq2, neg.log.10.adj.p,
group_A_rpm, #group_A_rpm_std,
group_B_rpm, #group_B_rpm_std,
MiRBase_ID, Family, Seed, Chromosome,
cell_marker, norm_adj, pCRC_adj)
# Create kable list with annotations
up_mirna <- up_mirna %>%
arrange(-lfc.deseq2) %>%
arrange(desc(cell_marker)) %>%
arrange(pCRC_adj) %>%
arrange(norm_adj) %>%
kable(col.names = c("miRNA", "LFC", "lfcSE", "FDR",
paste('RPM', tissue_type_A), #paste('std', tissue_type_A),
paste('RPM', tissue_type_B), #paste('std', tissue_type_B),
"miRBase_ID", "Family", "Seed", "Chr",
"Cell-Type Specific", 'Norm Background', 'pCRC Background'),
escape = F, booktabs = F, caption = paste("Upregulated in ", coef),
digits = c(0, 2, 2, 3, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0)) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T,
fixed_thead = list(enabled = T)) %>%
scroll_box(width = "2000px")
# create list of downregulated miRNA
down_mirna <- sig_list %>%
filter(lfc.deseq2 < -lfc.Threshold) %>%
# Annotate which miRNA are cell markers
mutate(
cell_marker = ifelse(signature_mirna %in% names(cell_spec_dict_inv), cell_spec_dict_inv[signature_mirna], '')) %>%
mutate(
cell_marker = cell_spec(cell_marker, color = ifelse(cell_marker != '', 'white', 'black'),
background = ifelse(cell_marker != '', 'blue', 'white'),
bold = ifelse(cell_marker != '', F, F)))
# Annotate which miRNA are in normal_adjacent
if (norm_adj_down != "None") {
down_mirna <- down_mirna %>%
mutate(
norm_adj = ifelse(signature_mirna %in% norm_adj_down, 'yes', '')) %>%
mutate(
norm_adj = cell_spec(norm_adj, color = ifelse(norm_adj == 'yes', 'white', 'black'),
background = ifelse(norm_adj == 'yes', 'black', 'white'),
bold = ifelse(norm_adj == 'yes', F, F))
)
}
else down_mirna$norm_adj <- "na"
# Annotate which miRNA are in pCRC_adjacent
if (pCRC_adj_down != "None") {
down_mirna <- down_mirna %>%
mutate(
pCRC_adj = ifelse(signature_mirna %in% pCRC_adj_down, 'yes', '')) %>%
mutate(
pCRC_adj = cell_spec(pCRC_adj, color = ifelse(pCRC_adj == 'yes', 'white', 'black'),
background = ifelse(pCRC_adj == 'yes', 'black', 'white'),
bold = ifelse(pCRC_adj == 'yes', F, F))
)
}
else down_mirna$pCRC_adj <- "na"
# number of upregulated miRNA
number_downregulated <- dim(down_mirna)[1]
down_mirna <- down_mirna %>% select(signature_mirna, lfc.deseq2, lfcSE.deseq2, neg.log.10.adj.p,
group_A_rpm, #group_A_rpm_std,
group_B_rpm, #group_B_rpm_std,
MiRBase_ID, Family, Seed, Chromosome,
cell_marker, norm_adj, pCRC_adj)
# Create kable list with annotations
down_mirna <- down_mirna %>%
arrange(lfc.deseq2) %>%
arrange(desc(cell_marker)) %>%
arrange(pCRC_adj) %>%
arrange(norm_adj) %>%
kable(col.names = c("miRNA", "LFC", "lfcSE", "FDR",
paste('RPM', tissue_type_A), #paste('std', tissue_type_A),
paste('RPM', tissue_type_B), #paste('std', tissue_type_B),
"miRBase_ID", "Family", "Seed", "Chr",
"Cell-Type Specific", 'Norm Background', 'pCRC Background'),
escape = F, booktabs = F, caption = paste("Downregulated in ", coef),
digits = c(0, 2, 2, 3, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0)) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T,
fixed_thead = list(enabled = T)) %>%
scroll_box(width = "2000px")
# Function return is to print kable, either upregulated or downregulated miRNA
return_list = list("up_mirna" = up_mirna, "down_mirna" = down_mirna,
"number_upregulated" = number_upregulated,
"number_downregulated" = number_downregulated)
return(return_list)
}
# Read the sample information into a data frame
sampleinfo <- read_delim("/Users/eirikhoy/Dropbox/projects/comet_analysis/data/sample_info_v9.csv", delim=';')
## Parsed with column specification:
## cols(
## filename = col_character(),
## paper = col_character(),
## sample_name = col_character(),
## type.tissue = col_character(),
## type = col_character(),
## tissue = col_character(),
## paper_sample_name = col_character(),
## `3p-adapter` = col_character(),
## qc_report = col_character(),
## malignant = col_character(),
## new_old = col_character()
## )
sampleinfo <- sampleinfo %>%
filter(qc_report == 'keep')
sampleinfo$filename <- str_remove(sampleinfo$filename, '.fasta.fas.gz.bam')
sampleinfo$filename <- str_remove(sampleinfo$filename, '.fasta.bam')
sampleinfo$filename <- str_replace_all(sampleinfo$filename, pattern = '\\.', replacement = '_')
sampleinfo$filename <- str_replace_all(sampleinfo$filename, pattern = '-', replacement = '_')
#sampleinfo$filename <- str_replace_all(sampleinfo$filename, pattern = '__', replacement = '_')
# Read the data into R
#seqdata <- read_delim("/Users/eirikhoy/Dropbox/projects/comet_analysis/data/count_matrix_08.12.20.csv", delim = ';')
seqdata_1 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-01-19_10-03-25/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
## .default = col_double(),
## miRNA = col_character()
## )
## See spec(...) for full column specifications.
#seqdata_2 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-01-19_12-04-26/miR.Counts.csv", delim = ',')
seqdata_3 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-01-19_15-00-38/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
## miRNA = col_character(),
## SRR1273998 = col_double(),
## SRR1273999 = col_double(),
## SRR1274000 = col_double(),
## SRR1274001 = col_double()
## )
seqdata_4 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-01-19_15-33-44/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
## .default = col_double(),
## miRNA = col_character()
## )
## See spec(...) for full column specifications.
seqdata_5 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-01-19_17-33-30/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
## .default = col_double(),
## miRNA = col_character()
## )
## See spec(...) for full column specifications.
seqdata_6 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-02-04_08-16-37/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
## .default = col_double(),
## miRNA = col_character()
## )
## See spec(...) for full column specifications.
seqdata_7 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-02-04_09-09-56/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
## .default = col_double(),
## miRNA = col_character()
## )
## See spec(...) for full column specifications.
seqdata_8 <- read_delim("/Users/eirikhoy/Dropbox/projects/mirge3/output_dir/miRge.2021-02-04_12-11-38/miR.Counts.csv", delim = ',')
## Parsed with column specification:
## cols(
## .default = col_double(),
## miRNA = col_character()
## )
## See spec(...) for full column specifications.
seqdata <- inner_join(inner_join(inner_join(inner_join(inner_join(inner_join(seqdata_1, seqdata_3), seqdata_4), seqdata_5), seqdata_6), seqdata_7), seqdata_8)
## Joining, by = "miRNA"
## Joining, by = "miRNA"
## Joining, by = "miRNA"
## Joining, by = "miRNA"
## Joining, by = "miRNA"
## Joining, by = "miRNA"
colnames(seqdata) <- str_replace_all(colnames(seqdata), pattern='-', replacement = '_')
#colnames(seqdata) <- str_replace_all(colnames(seqdata), pattern='__', replacement = '_')
seqdata <- seqdata[- grep("\\*", seqdata$miRNA), ]
seqdata <- seqdata %>% filter(str_detect(miRNA , "chr", negate = TRUE))
#colnames(seqdata)[2:length(colnames(seqdata))] <- sampleinfo$sample
# Format the data
countdata <- seqdata %>%
column_to_rownames("miRNA") %>%
#rename_all(str_remove, ".bam") %>%
select(sampleinfo$filename) %>%
as.matrix()
# List consensus samples
consensus <- c("neerincx", "fromm", "schee", "selitsky")
# create the design formula
sampleinfo$tissue.type <- as.factor(paste(sampleinfo$type, sampleinfo$tissue, sep="."))
sampleinfo$type <- as.factor(sampleinfo$type)
design <- as.formula(~ tissue.type)
Differential Expression
# Make a named list of signature miRNA
dict_sig_mirna <- c()
res_dict <- list()
ref <- 'normal.colorect'
dds <- DeseqObject(design, countdata, sampleinfo, "None", "None", ref)
# #datasets in total
dim(dds[, colData(dds)$type.tissue == 'pCRC'])
## [1] 389 120
dim(dds[, colData(dds)$type.tissue == 'mLi'])
## [1] 389 35
dim(dds[, colData(dds)$type.tissue == 'mLu'])
## [1] 389 28
dim(dds[, colData(dds)$type.tissue == 'nCR'])
## [1] 389 25
dim(dds[, colData(dds)$type.tissue == 'nLi'])
## [1] 389 20
dim(dds[, colData(dds)$type.tissue == 'nLu'])
## [1] 389 10
dim(dds[, colData(dds)$type.tissue == 'PM'])
## [1] 389 30
# #datasets for Fromm
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'fromm'])
## [1] 389 3
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'fromm'])
## [1] 389 19
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'fromm'])
## [1] 389 24
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'fromm'])
## [1] 389 3
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'fromm'])
## [1] 389 8
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'fromm'])
## [1] 389 7
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'fromm'])
## [1] 389 18
# #datasets for Schee
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'schee'])
## [1] 389 83
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'schee'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'schee'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'schee'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'schee'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'schee'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'schee'])
## [1] 389 0
# #datasets for Schee
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'neerincx'])
## [1] 389 34
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'neerincx'])
## [1] 389 16
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'neerincx'])
## [1] 389 4
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'neerincx'])
## [1] 389 22
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'neerincx'])
## [1] 389 9
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'neerincx'])
## [1] 389 3
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'neerincx'])
## [1] 389 12
# #datasets for Schee
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'selitsky'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'selitsky'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'selitsky'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'selitsky'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'selitsky'])
## [1] 389 3
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'selitsky'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'selitsky'])
## [1] 389 0
## Plot dispersion estimates
plotDispEsts(dds)

McCall, Matthew N; Kim, Min-Sik; Adil, Mohammed; Patil, Arun H; Lu, Yin; Mitchell, Christopher J; Leal-Rojas, Pamela; Xu, Jinchong; Kumar, Manoj; Dawson, Valina L; Dawson, Ted M; Baras, Alexander S; Rosenberg, Avi Z; Arking, Dan E; Burns, Kathleen H; Pandey, Akhilesh; Halushka, Marc K Toward the human cellular microRNAome Genome Res. October 2017
cell_spec_dict <- list(
"CD14+ Monocyte" = c("Hsa-Mir-15-P1a_5p","Hsa-Mir-15-P1b_5p", "Hsa-Mir-17-P1a_5p/P1b_5p"),
"Dendritic Cell" = c("Hsa-Mir-146-P2_5p", "Hsa-Mir-342_3p", "Hsa-Mir-142_3p",
"Hsa-Mir-223_3p"),
"Endothelial Cell" = c("Hsa-Mir-126_5p"),
"Epithelial Cell" = c("Hsa-Mir-8-P2a_3p", "Hsa-Mir-8-P2b_3p", "Hsa0Mir-205-P1_5p",
"Hsa-Mir-192-P1_5p/P2_5p", "Hsa-Mir-375_3p"),
"Islet Cell" = c("Hsa-Mir-375_3p", "Hsa-Mir-154-P7_5p", "Hsa-Mir-7-P1_5p/P2_5p/P3_5p"),
"Lymphocyte" = c("Hsa-Mir-146-P2_5p", "Hsa-Mir-342_3p", "Hsa-Mir-150_5p",
"Hsa-Mir-155_5p"),
"Macrophage" = c("Hsa-Mir-342_3p", "Hsa-Mir-142_3p", "Hsa-Mir-223_3p",
"Hsa-Mir-155_5p", "Hsa-Mir-24-P1_3p/P2_3p",
"Hsa-Mir-185_5p"),
"Melanocyte" = c("Hsa-Mir-185_5p", "Hsa-Mir-204-P2_5p"),
"Mesenchymal" = c("Hsa-Mir-185_5p", "Hsa-Mir-143_3p", "Hsa-Mir-145_5p"),
"Neural" = c("Hsa-Mir-375_3p", "Hsa-Mir-154-P7_5p", "Hsa-Mir-7-P1_5p/P2_5p/P3_5p",
"Hsa-Mir-128-P1_3p/P2_3p", "Hsa-Mir-129-P1_5p/P2_5p",
"Hsa-Mir-9-P1_5p/P2_5p/P3_5p","Hsa-Mir-430-P2_3p",
"Hsa-Mir-430-P4_3p"),
"Platelet" = c("Hsa-Mir-126_5p", "Hsa-Mir-486_5p"),
"Red Blood Cell" = c("Hsa-Mir-486_5p", "Hsa-Mir-451_5p", "Hsa-Mir-144_5p"),
"Retinal Epithelial Cell" = c("Hsa-Mir-204-P1_5p", "Hsa-Mir-204-P2_5p", "Hsa-Mir-335_5p"),
"Skeletal Myocyte" = c("Hsa-Mir-1-P1_3p/P2_3p", "Hsa-Mir-133-P1_3p/P2_3p/P3_3p"),
"Stem Cell" = c("Hsa-Mir-430-P2_3p", "Hsa-Mir-430-P4_3p", "Hsa-Mir-133-P1_3p/P2_3p/P3_3p"),
"Hepatocyte" = c("Hsa-Mir-122_5p")
)
cell_spec_dict_inv <- topGO::inverseList(cell_spec_dict)
##
Pritchard, C C; Kroh, E; Wood, B; Arroyo, J D; Dougherty, K J; Miyaji, M M; Tait, J F; Tewari, M Blood Cell Origin of Circulating MicroRNAs: A Cautionary Note for Cancer Biomarker Studies Cancer Prevention Research 2012
blood.cell.mirna <- c("Hsa-Mir-223_3p",
"Hsa-Mir-15-P2a_5p",
"Hsa-Mir-15-P2b_5p",
"Hsa-Mir-126-v1_3p",
"Hsa-Mir-142-v1_3p",
"Hsa-Mir-21_5p",
"Hsa-Mir-24-P1_3p",
"Hsa-Mir-24-P2_3p",
"Hsa-Mir-19-P2a_3p",
"Hsa-Mir-19-P2b_3p",
"Hsa-Mir-103-P1_3p",
"Hsa-Mir-103-P2_3p",
"Hsa-Let-7-P1a_5p",
"Hsa-Let-7-P2a1_5p",
"Hsa-Let-7-P2a2_5p",
"Hsa-Mir-451_5p",
"Hsa-Mir-92-P1a_3p",
"Hsa-Mir-92-P1b_3p",
"Hsa-Mir-17-P1b_5p",
"Hsa-Mir-19-P1_3p",
"Hsa-Mir-30-P2c_5p",
"Hsa-Mir-17-P1a",
"Hsa-Mir-15-P1b_5p",
"Hsa-Mir-103-P3_3p",
"Hsa-Let-7-P2a3_5p",
"Hsa-Let-7-P2b1_5p",
"Hsa-Mir-221-P1_3p",
"Hsa-Mir-221-P2_3p",
"Hsa-Mir-17-P1c_5p",
"Hsa-Mir-30-P2a_5p",
"Hsa-Mir-30-P2b_5p",
"Hsa-Mir-28-P2_5p",
"Hsa-Mir-30-P1b_5p",
"Hsa-Mir-30-P1c_5p",
"Hsa-Mir-486_5p",
"Hsa-Mir-92-P2c_3p",
"Hsa-Mir-181-P1a_5p",
"Hsa-Mir-181-P1b_5p",
"Hsa-Mir-146-P1_5p",
"Hsa-Let-7-P2c1_5p",
"Hsa-Mir-197_3p",
"Hsa-Mir-17-P3c_5p",
"Hsa-Mir-17-P3c_3p",
"Hsa-Mir-148-P3_3p",
"Hsa-Mir-766_3p",
"Hsa-Mir-17-P3b_5p",
"Hsa-Mir-328_3p",
"Hsa-Mir-574_3p",
"Hsa-Mir-155_5p",
"Hsa-Mir-425_5p",
"Hsa-Mir-148-P1_3p",
"Hsa-Mir-29-P1a_3p",
"Hsa-Mir-8-P2b_3p",
"Hsa-Mir-92-P1c_3p",
"Hsa-Mir-192-P2_5p",
"Hsa-Mir-362-P2-v1_3p",
"Hsa-Mir-362-P5_5p"
)
nCR vs nLi
column='tissue.type'
tissue_type_A <- 'normal.liver'
tissue_type_B <- 'normal.colorect'
norm_adj_up = "None"
norm_adj_down = "None"
pCRC_adj_up = "None"
pCRC_adj_down = "None"
coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
lfc.Threshold, rpm.Threshold,
norm_adj_up,
norm_adj_down)
dict_sig_mirna[paste(coef, "up", sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
tissue_type_A, tissue_type_B,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_normal.liver_vs_normal.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM normal.liver
|
RPM normal.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-204-P1_5p
|
1.56
|
0.51
|
4.46e-03
|
190
|
43
|
hsa-mir-204
|
MIR-204
|
UCCCUUU
|
chr9
|
Retinal Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-335_5p
|
1.18
|
0.17
|
2.56e-11
|
205
|
79
|
hsa-mir-335
|
MIR-335
|
CAAGAGC
|
chr7
|
Retinal Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-144_5p
|
1.53
|
0.33
|
5.21e-05
|
206
|
65
|
hsa-mir-144
|
MIR-144
|
GAUAUCA
|
chr17
|
Red Blood Cell
|
na
|
na
|
|
Hsa-Mir-128-P1_3p/P2_3p
|
0.65
|
0.16
|
2.62e-04
|
194
|
110
|
hsa-mir-128-1
|
MIR-128
|
CACAGUG
|
chr2
|
Neural
|
na
|
na
|
|
Hsa-Mir-122_5p
|
10.58
|
0.78
|
7.97e-28
|
148842
|
26
|
hsa-mir-122
|
MIR-122
|
GGAGUGU
|
chr18
|
Hepatocyte
|
na
|
na
|
|
Hsa-Mir-486_5p
|
1.34
|
0.36
|
2.99e-03
|
10617
|
3961
|
hsa-mir-486-1
|
MIR-486
|
CCUGUAC
|
chr8
|
c(“Platelet”, “Red Blood Cell”)
|
na
|
na
|
|
Hsa-Mir-126_5p
|
1.25
|
0.21
|
1.87e-08
|
11148
|
4230
|
hsa-mir-126
|
MIR-126
|
AUUAUUA
|
chr9
|
c(“Endothelial Cell”, “Platelet”)
|
na
|
na
|
|
Hsa-Mir-885_5p
|
8.77
|
0.70
|
8.60e-24
|
510
|
0
|
hsa-mir-885
|
MIR-885
|
CCAUUAC
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-483_5p
|
4.03
|
0.71
|
1.04e-10
|
115
|
1
|
hsa-mir-483
|
MIR-483
|
AGACGGG
|
chr11
|
|
na
|
na
|
|
Hsa-Let-7-P1c_5p
|
3.18
|
0.28
|
1.60e-27
|
4945
|
466
|
hsa-let-7c
|
LET-7
|
GAGGUAG
|
chr21
|
|
na
|
na
|
|
Hsa-Mir-10-P2c_5p
|
3.08
|
0.33
|
6.34e-19
|
1202
|
114
|
hsa-mir-99a
|
MIR-10
|
ACCCGUA
|
chr21
|
|
na
|
na
|
|
Hsa-Mir-455_5p
|
2.48
|
0.18
|
9.25e-41
|
279
|
44
|
hsa-mir-455
|
MIR-455
|
AUGUGCC
|
chr9
|
|
na
|
na
|
|
Hsa-Mir-193-P2a_3p/P2b_3p
|
2.37
|
0.23
|
5.29e-22
|
219
|
37
|
hsa-mir-365b
|
MIR-193
|
AAUGCCC
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-193-P1b_3p
|
2.34
|
0.23
|
2.49e-22
|
514
|
90
|
hsa-mir-193b
|
MIR-193
|
ACUGGCC
|
chr16
|
|
na
|
na
|
|
Hsa-Mir-15-P1d_5p
|
2.26
|
0.30
|
5.34e-14
|
247
|
40
|
hsa-mir-424
|
MIR-15
|
AGCAGCA
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-193-P1a_5p
|
2.14
|
0.28
|
3.66e-12
|
298
|
57
|
hsa-mir-193a
|
MIR-193
|
GGGUCUU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-10-P3c_5p
|
2.12
|
0.30
|
6.48e-11
|
1620
|
326
|
hsa-mir-125b-2
|
MIR-10
|
CCCUGAG
|
chr21
|
|
na
|
na
|
|
Hsa-Mir-139_5p
|
2.00
|
0.26
|
1.06e-11
|
174
|
41
|
hsa-mir-139
|
MIR-139
|
CUACAGU
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-148-P1_3p
|
2.00
|
0.24
|
8.34e-15
|
105086
|
21730
|
hsa-mir-148a
|
MIR-148
|
CAGUGCA
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-10-P3a_5p
|
1.93
|
0.31
|
1.24e-08
|
611
|
137
|
hsa-mir-125b-1
|
MIR-10
|
CCCUGAG
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-423_5p
|
1.62
|
0.24
|
1.23e-09
|
1198
|
345
|
hsa-mir-423
|
MIR-423
|
GAGGGGC
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-92-P1a_3p/P1b_3p
|
1.60
|
0.22
|
1.98e-12
|
51173
|
13849
|
hsa-mir-92a-1
|
MIR-92
|
AUUGCAC
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-101-P1_3p/P2_3p
|
1.58
|
0.18
|
3.44e-17
|
14649
|
4197
|
hsa-mir-101-1
|
MIR-101
|
UACAGUA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-22-P1a_3p
|
1.58
|
0.17
|
1.76e-18
|
78932
|
23453
|
hsa-mir-22
|
MIR-22
|
AGCUGCC
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-340_5p
|
1.42
|
0.16
|
3.45e-18
|
1030
|
337
|
hsa-mir-340
|
MIR-340
|
UAUAAAG
|
chr5
|
|
na
|
na
|
|
Hsa-Mir-30-P1a_5p
|
1.32
|
0.23
|
1.07e-07
|
15175
|
5150
|
hsa-mir-30a
|
MIR-30
|
GUAAACA
|
chr6
|
|
na
|
na
|
|
Hsa-Mir-744_5p
|
1.31
|
0.21
|
1.28e-08
|
165
|
59
|
hsa-mir-744
|
MIR-744
|
GCGGGGC
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-574_3p
|
1.21
|
0.19
|
1.27e-08
|
745
|
305
|
hsa-mir-574
|
MIR-574
|
ACGCUCA
|
chr4
|
|
na
|
na
|
|
Hsa-Mir-130-P1a_3p
|
1.09
|
0.20
|
4.15e-07
|
907
|
379
|
hsa-mir-130a
|
MIR-130
|
AGUGCAA
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-10-P2a_5p
|
1.07
|
0.32
|
3.39e-03
|
2634
|
986
|
hsa-mir-100
|
MIR-10
|
ACCCGUA
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-30-P2a_5p/P2b_5p/P2c_5p
|
0.99
|
0.15
|
4.69e-10
|
5891
|
2677
|
hsa-mir-30c-2
|
MIR-30
|
GUAAACA
|
chr6
|
|
na
|
na
|
|
Hsa-Mir-154-P23_3p
|
0.89
|
0.20
|
5.21e-05
|
222
|
108
|
hsa-mir-654
|
MIR-154
|
AUGUCUG
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-197_3p
|
0.79
|
0.19
|
2.06e-04
|
344
|
180
|
hsa-mir-197
|
MIR-197
|
UCACCAC
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-214_3p
|
0.73
|
0.23
|
5.91e-03
|
137
|
74
|
hsa-mir-214
|
MIR-214
|
CAGCAGG
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-30-P1b_5p
|
0.71
|
0.15
|
7.77e-06
|
11240
|
6046
|
hsa-mir-30e
|
MIR-30
|
GUAAACA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-30-P1c_5p
|
0.70
|
0.16
|
7.79e-05
|
13680
|
7243
|
hsa-mir-30d
|
MIR-30
|
GUAAACA
|
chr8
|
|
na
|
na
|
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 36
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_normal.liver_vs_normal.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM normal.liver
|
RPM normal.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-145_5p
|
-2.51
|
0.32
|
5.55e-15
|
484
|
2832
|
hsa-mir-145
|
MIR-145
|
UCCAGUU
|
chr5
|
Mesenchymal
|
na
|
na
|
|
Hsa-Mir-143_3p
|
-2.35
|
0.27
|
1.80e-17
|
37572
|
164812
|
hsa-mir-143
|
MIR-143
|
GAGAUGA
|
chr5
|
Mesenchymal
|
na
|
na
|
|
Hsa-Mir-24-P1_3p/P2_3p
|
-0.63
|
0.20
|
5.98e-03
|
319
|
413
|
hsa-mir-24-2
|
MIR-24
|
GGCUCAG
|
chr19
|
Macrophage
|
na
|
na
|
|
Hsa-Mir-8-P2b_3p
|
-6.38
|
0.23
|
7.72e-155
|
31
|
2444
|
hsa-mir-200c
|
MIR-8
|
AAUACUG
|
chr12
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-8-P2a_3p
|
-4.47
|
0.24
|
4.08e-76
|
369
|
7741
|
hsa-mir-200b
|
MIR-8
|
AAUACUG
|
chr1
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-192-P1_5p/P2_5p
|
-0.72
|
0.32
|
1.03e-02
|
29546
|
46010
|
hsa-mir-192
|
MIR-192
|
UGACCUA
|
chr11
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-15-P1b_5p
|
-0.68
|
0.16
|
1.18e-04
|
114
|
168
|
hsa-mir-15b
|
MIR-15
|
AGCAGCA
|
chr3
|
CD14+ Monocyte
|
na
|
na
|
|
Hsa-Mir-133-P1_3p/P2_3p/P3_3p
|
-4.10
|
0.38
|
9.37e-29
|
22
|
460
|
hsa-mir-133a-2
|
MIR-133
|
UUGGUCC
|
chr20
|
c(“Skeletal Myocyte”, “Stem Cell”)
|
na
|
na
|
|
Hsa-Mir-155_5p
|
-1.51
|
0.26
|
8.41e-08
|
113
|
295
|
hsa-mir-155
|
MIR-155
|
UAAUGCU
|
chr21
|
c(“Lymphocyte”, “Macrophage”)
|
na
|
na
|
|
Hsa-Mir-375_3p
|
-2.65
|
0.37
|
1.69e-12
|
2553
|
17215
|
hsa-mir-375
|
MIR-375
|
UUGUUCG
|
chr2
|
c(“Epithelial Cell”, “Islet Cell”, “Neural”)
|
na
|
na
|
|
Hsa-Mir-196-P1_5p/P2_5p
|
-6.76
|
0.34
|
3.97e-79
|
2
|
250
|
hsa-mir-196a-1
|
MIR-196
|
AGGUAGU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-147_3p
|
-6.57
|
0.38
|
2.57e-64
|
1
|
110
|
hsa-mir-147b
|
MIR-147
|
UGUGCGG
|
chr15
|
|
na
|
na
|
|
Hsa-Mir-577_5p
|
-6.02
|
0.34
|
1.65e-65
|
2
|
138
|
hsa-mir-577
|
MIR-577
|
UAGAUAA
|
chr4
|
|
na
|
na
|
|
Hsa-Mir-8-P1b_3p
|
-6.00
|
0.30
|
3.97e-79
|
154
|
9180
|
hsa-mir-141
|
MIR-8
|
AACACUG
|
chr12
|
|
na
|
na
|
|
Hsa-Mir-196-P3_5p
|
-5.53
|
0.37
|
2.24e-42
|
9
|
400
|
hsa-mir-196b
|
MIR-196
|
AGGUAGU
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-10-P1b_5p
|
-4.80
|
0.36
|
6.31e-38
|
2704
|
75067
|
hsa-mir-10b
|
MIR-10
|
ACCCUGU
|
chr2
|
|
na
|
na
|
|
Hsa-Mir-8-P3a_3p
|
-4.32
|
0.24
|
2.00e-68
|
68
|
1282
|
hsa-mir-429
|
MIR-8
|
AAUACUG
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-8-P1a_3p
|
-4.10
|
0.24
|
4.38e-64
|
108
|
1754
|
hsa-mir-200a
|
MIR-8
|
AACACUG
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-190-P1_5p
|
-3.71
|
0.26
|
4.48e-45
|
23
|
294
|
hsa-mir-190a
|
MIR-190
|
GAUAUGU
|
chr15
|
|
na
|
na
|
|
Hsa-Mir-96-P3_5p
|
-3.57
|
0.32
|
1.72e-22
|
27
|
224
|
hsa-mir-183
|
MIR-96
|
AUGGCAC
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-203_3p
|
-2.68
|
0.28
|
6.56e-18
|
171
|
922
|
hsa-mir-203a
|
MIR-203
|
UGAAAUG
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-96-P2_5p
|
-2.65
|
0.24
|
2.99e-23
|
370
|
1867
|
hsa-mir-182
|
MIR-96
|
UUGGCAA
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-221-P1_3p
|
-2.30
|
0.19
|
5.81e-32
|
217
|
964
|
hsa-mir-221
|
MIR-221
|
GCUACAU
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-338-P1_3p
|
-1.89
|
0.31
|
1.21e-08
|
48
|
166
|
hsa-mir-338
|
MIR-338
|
CCAGCAU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-221-P2_3p
|
-1.87
|
0.20
|
9.25e-18
|
173
|
574
|
hsa-mir-222
|
MIR-221
|
GCUACAU
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-10-P1a_5p
|
-1.77
|
0.30
|
1.66e-07
|
25828
|
70250
|
hsa-mir-10a
|
MIR-10
|
ACCCUGU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-146-P1_5p
|
-1.69
|
0.34
|
9.05e-06
|
351
|
891
|
hsa-mir-146a
|
MIR-146
|
GAGAACU
|
chr5
|
|
na
|
na
|
|
Hsa-Mir-92-P1c_3p
|
-1.32
|
0.26
|
4.00e-06
|
303
|
634
|
hsa-mir-92b
|
MIR-92
|
AUUGCAC
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-181-P1c_5p
|
-1.25
|
0.21
|
6.34e-08
|
261
|
527
|
hsa-mir-181c
|
MIR-181
|
ACAUUCA
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-425_5p
|
-1.16
|
0.19
|
6.36e-09
|
247
|
499
|
hsa-mir-425
|
MIR-425
|
AUGACAC
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-362-P3_3p
|
-1.15
|
0.36
|
2.88e-03
|
60
|
103
|
hsa-mir-501
|
MIR-362
|
AUGCACC
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-192-P1_5p
|
-1.10
|
0.27
|
7.22e-05
|
109276
|
212293
|
hsa-mir-192
|
MIR-192
|
UGACCUA
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-10-P2b_5p
|
-1.08
|
0.36
|
6.81e-03
|
1478
|
2499
|
hsa-mir-99b
|
MIR-10
|
ACCCGUA
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-210_3p
|
-1.07
|
0.28
|
1.53e-03
|
106
|
185
|
hsa-mir-210
|
MIR-210
|
UGUGCGU
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-132-P1_3p
|
-1.03
|
0.20
|
2.09e-06
|
61
|
117
|
hsa-mir-132
|
MIR-132
|
AACAGUC
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-194-P1_5p/P2_5p
|
-0.94
|
0.27
|
5.19e-04
|
6323
|
11619
|
hsa-mir-194-2
|
MIR-194
|
GUAACAG
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-130-P4a_3p
|
-0.84
|
0.13
|
2.77e-10
|
70
|
111
|
hsa-mir-454
|
MIR-130
|
AGUGCAA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-188-P2_5p
|
-0.82
|
0.19
|
9.56e-05
|
279
|
427
|
hsa-mir-532
|
MIR-188
|
AUGCCUU
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-191_5p
|
-0.78
|
0.23
|
1.91e-03
|
10721
|
14608
|
hsa-mir-191
|
MIR-191
|
AACGGAA
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-21_5p
|
-0.76
|
0.17
|
1.54e-04
|
19240
|
28173
|
hsa-mir-21
|
MIR-21
|
AGCUUAU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-130-P2a_3p
|
-0.71
|
0.20
|
3.57e-03
|
92
|
131
|
hsa-mir-301a
|
MIR-130
|
AGUGCAA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-1307_3p
|
-0.66
|
0.19
|
1.89e-03
|
89
|
127
|
hsa-mir-1307
|
MIR-1307
|
CGACCGG
|
chr10
|
|
na
|
na
|
|
Hsa-Mir-362-P2_3p/P4_3p
|
-0.61
|
0.20
|
7.29e-03
|
211
|
268
|
hsa-mir-500a
|
MIR-362
|
UGCACCU
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-1307_5p
|
-0.61
|
0.26
|
3.36e-02
|
502
|
713
|
hsa-mir-1307
|
MIR-1307
|
CGACCGG
|
chr10
|
|
na
|
na
|
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 44
nCR vs nLu
column='tissue.type'
tissue_type_A <- 'normal.lung'
tissue_type_B <- 'normal.colorect'
norm_adj_up = "None"
norm_adj_down = "None"
pCRC_adj_up = "None"
pCRC_adj_down = "None"
coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
lfc.Threshold, rpm.Threshold,
norm_adj_up,
norm_adj_down)
dict_sig_mirna[paste(coef, "up", sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
tissue_type_A, tissue_type_B,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_normal.lung_vs_normal.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM normal.lung
|
RPM normal.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-335_5p
|
1.48
|
0.21
|
3.73e-11
|
300
|
79
|
hsa-mir-335
|
MIR-335
|
CAAGAGC
|
chr7
|
Retinal Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-144_5p
|
2.32
|
0.41
|
5.46e-07
|
451
|
65
|
hsa-mir-144
|
MIR-144
|
GAUAUCA
|
chr17
|
Red Blood Cell
|
na
|
na
|
|
Hsa-Mir-451_5p
|
1.90
|
0.47
|
1.06e-03
|
12496
|
2815
|
hsa-mir-451a
|
MIR-451
|
AACCGUU
|
chr17
|
Red Blood Cell
|
na
|
na
|
|
Hsa-Mir-24-P1_3p/P2_3p
|
0.86
|
0.25
|
1.88e-03
|
1084
|
413
|
hsa-mir-24-2
|
MIR-24
|
GGCUCAG
|
chr19
|
Macrophage
|
na
|
na
|
|
Hsa-Mir-486_5p
|
2.04
|
0.45
|
1.33e-04
|
22081
|
3961
|
hsa-mir-486-1
|
MIR-486
|
CCUGUAC
|
chr8
|
c(“Platelet”, “Red Blood Cell”)
|
na
|
na
|
|
Hsa-Mir-126_5p
|
2.57
|
0.26
|
4.06e-21
|
33620
|
4230
|
hsa-mir-126
|
MIR-126
|
AUUAUUA
|
chr9
|
c(“Endothelial Cell”, “Platelet”)
|
na
|
na
|
|
Hsa-Mir-146-P2_5p
|
1.65
|
0.41
|
1.94e-04
|
17435
|
3259
|
hsa-mir-146b
|
MIR-146
|
GAGAACU
|
chr10
|
c(“Dendritic Cell”, “Lymphocyte”)
|
na
|
na
|
|
Hsa-Mir-342_3p
|
1.00
|
0.31
|
6.94e-03
|
681
|
260
|
hsa-mir-342
|
MIR-342
|
CUCACAC
|
chr14
|
c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”)
|
na
|
na
|
|
Hsa-Mir-34-P2b_5p
|
5.29
|
0.43
|
1.18e-33
|
748
|
11
|
hsa-mir-34c
|
MIR-34
|
GGCAGUG
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-34-P2a_5p
|
4.98
|
0.49
|
3.17e-22
|
116
|
2
|
hsa-mir-34b
|
MIR-34
|
GGCAGUG
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-184_3p
|
4.75
|
0.92
|
5.98e-06
|
111
|
1
|
hsa-mir-184
|
MIR-184
|
GGACGGA
|
chr15
|
|
na
|
na
|
|
Hsa-Mir-30-P1a_5p
|
3.04
|
0.28
|
6.17e-24
|
60860
|
5150
|
hsa-mir-30a
|
MIR-30
|
GUAAACA
|
chr6
|
|
na
|
na
|
|
Hsa-Mir-218-P1_5p/P2_5p
|
2.39
|
0.33
|
7.14e-11
|
374
|
52
|
hsa-mir-218-1
|
MIR-218
|
UGUGCUU
|
chr4
|
|
na
|
na
|
|
Hsa-Mir-10-P2c_5p
|
2.23
|
0.41
|
4.40e-07
|
804
|
114
|
hsa-mir-99a
|
MIR-10
|
ACCCGUA
|
chr21
|
|
na
|
na
|
|
Hsa-Mir-181-P1a_5p/P1b_5p
|
2.19
|
0.24
|
1.16e-18
|
80063
|
12321
|
hsa-mir-181a-1
|
MIR-181
|
ACAUUCA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-181-P2a_5p/P2b_5p
|
2.06
|
0.23
|
2.25e-18
|
3799
|
650
|
hsa-mir-181b-1
|
MIR-181
|
ACAUUCA
|
chr1
|
|
na
|
na
|
|
Hsa-Let-7-P1c_5p
|
1.97
|
0.35
|
1.57e-07
|
2606
|
466
|
hsa-let-7c
|
LET-7
|
GAGGUAG
|
chr21
|
|
na
|
na
|
|
Hsa-Mir-130-P1a_3p
|
1.92
|
0.25
|
5.89e-13
|
1941
|
379
|
hsa-mir-130a
|
MIR-130
|
AGUGCAA
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-30-P1c_5p
|
1.74
|
0.20
|
4.37e-16
|
34088
|
7243
|
hsa-mir-30d
|
MIR-30
|
GUAAACA
|
chr8
|
|
na
|
na
|
|
Hsa-Mir-101-P1_3p/P2_3p
|
1.45
|
0.22
|
1.42e-09
|
16084
|
4197
|
hsa-mir-101-1
|
MIR-101
|
UACAGUA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-338-P1_3p
|
1.45
|
0.39
|
2.15e-03
|
605
|
166
|
hsa-mir-338
|
MIR-338
|
CCAGCAU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-181-P2c_5p
|
1.42
|
0.28
|
1.14e-06
|
200
|
51
|
hsa-mir-181d
|
MIR-181
|
ACAUUCA
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-10-P2a_5p
|
1.40
|
0.40
|
2.08e-03
|
4056
|
986
|
hsa-mir-100
|
MIR-10
|
ACCCGUA
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-10-P3a_5p
|
1.37
|
0.38
|
1.96e-03
|
507
|
137
|
hsa-mir-125b-1
|
MIR-10
|
CCCUGAG
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-10-P3b_5p
|
1.34
|
0.34
|
7.55e-04
|
8561
|
2392
|
hsa-mir-125a
|
MIR-10
|
CCCUGAG
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-181-P1c_5p
|
1.25
|
0.26
|
5.14e-06
|
1790
|
527
|
hsa-mir-181c
|
MIR-181
|
ACAUUCA
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-140_3p
|
1.25
|
0.19
|
1.88e-09
|
2195
|
686
|
hsa-mir-140
|
MIR-140
|
CCACAGG
|
chr16
|
|
na
|
na
|
|
Hsa-Let-7-P2b1_5p
|
1.10
|
0.15
|
1.06e-11
|
6253
|
2110
|
hsa-let-7f-1
|
LET-7
|
GAGGUAG
|
chr9
|
|
na
|
na
|
|
Hsa-Mir-30-P2a_5p/P2b_5p/P2c_5p
|
1.08
|
0.19
|
9.04e-08
|
7603
|
2677
|
hsa-mir-30c-2
|
MIR-30
|
GUAAACA
|
chr6
|
|
na
|
na
|
|
Hsa-Mir-221-P1_3p
|
0.95
|
0.23
|
1.40e-04
|
2502
|
964
|
hsa-mir-221
|
MIR-221
|
GCUACAU
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-92-P1c_3p
|
0.90
|
0.32
|
1.56e-02
|
1737
|
634
|
hsa-mir-92b
|
MIR-92
|
AUUGCAC
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-15-P2c_5p
|
0.86
|
0.30
|
2.04e-02
|
1552
|
672
|
hsa-mir-195
|
MIR-15
|
AGCAGCA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-374-P2_5p
|
0.80
|
0.25
|
3.59e-03
|
148
|
65
|
hsa-mir-374b
|
MIR-374
|
UAUAAUA
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-221-P2_3p
|
0.77
|
0.25
|
5.86e-03
|
1346
|
574
|
hsa-mir-222
|
MIR-221
|
GCUACAU
|
chrX
|
|
na
|
na
|
|
Hsa-Let-7-P2c2_5p
|
0.76
|
0.21
|
1.16e-03
|
5088
|
2218
|
hsa-let-7i
|
LET-7
|
GAGGUAG
|
chr12
|
|
na
|
na
|
|
Hsa-Mir-130-P2a_3p
|
0.75
|
0.25
|
6.48e-03
|
305
|
131
|
hsa-mir-301a
|
MIR-130
|
AGUGCAA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-652_3p
|
0.71
|
0.20
|
1.38e-03
|
187
|
86
|
hsa-mir-652
|
MIR-652
|
AUGGCGC
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-26-P1_5p/P2_5p
|
0.68
|
0.17
|
3.54e-04
|
123947
|
56268
|
hsa-mir-26b
|
MIR-26
|
UCAAGUA
|
chr2
|
|
na
|
na
|
|
Hsa-Mir-28-P2_3p
|
0.63
|
0.21
|
9.02e-03
|
6007
|
2638
|
hsa-mir-151a
|
MIR-28
|
CGAGGAG
|
chr8
|
|
na
|
na
|
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 39
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_normal.lung_vs_normal.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM normal.lung
|
RPM normal.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-143_3p
|
-0.81
|
0.34
|
2.00e-02
|
133600
|
164812
|
hsa-mir-143
|
MIR-143
|
GAGAUGA
|
chr5
|
Mesenchymal
|
na
|
na
|
|
Hsa-Mir-192-P1_5p/P2_5p
|
-8.51
|
0.40
|
3.64e-97
|
142
|
46010
|
hsa-mir-192
|
MIR-192
|
UGACCUA
|
chr11
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-8-P2a_3p
|
-4.00
|
0.29
|
6.74e-40
|
618
|
7741
|
hsa-mir-200b
|
MIR-8
|
AAUACUG
|
chr1
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-8-P2b_3p
|
-2.06
|
0.29
|
1.04e-11
|
766
|
2444
|
hsa-mir-200c
|
MIR-8
|
AAUACUG
|
chr12
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-17-P1a_5p/P1b_5p
|
-0.77
|
0.24
|
9.07e-03
|
184
|
225
|
hsa-mir-17
|
MIR-17
|
AAAGUGC
|
chr13
|
CD14+ Monocyte
|
na
|
na
|
|
Hsa-Mir-133-P1_3p/P2_3p/P3_3p
|
-2.33
|
0.47
|
9.21e-08
|
92
|
460
|
hsa-mir-133a-2
|
MIR-133
|
UUGGUCC
|
chr20
|
c(“Skeletal Myocyte”, “Stem Cell”)
|
na
|
na
|
|
Hsa-Mir-375_3p
|
-3.01
|
0.47
|
2.15e-10
|
2346
|
17215
|
hsa-mir-375
|
MIR-375
|
UUGUUCG
|
chr2
|
c(“Epithelial Cell”, “Islet Cell”, “Neural”)
|
na
|
na
|
|
Hsa-Mir-194-P1_5p/P2_5p
|
-7.89
|
0.34
|
2.93e-114
|
58
|
11619
|
hsa-mir-194-2
|
MIR-194
|
GUAACAG
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-192-P1_5p
|
-7.28
|
0.34
|
3.81e-95
|
1684
|
212293
|
hsa-mir-192
|
MIR-192
|
UGACCUA
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-196-P1_5p/P2_5p
|
-6.68
|
0.41
|
1.42e-54
|
3
|
250
|
hsa-mir-196a-1
|
MIR-196
|
AGGUAGU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-196-P3_5p
|
-5.95
|
0.47
|
9.59e-33
|
7
|
400
|
hsa-mir-196b
|
MIR-196
|
AGGUAGU
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-577_5p
|
-5.95
|
0.41
|
4.39e-45
|
3
|
138
|
hsa-mir-577
|
MIR-577
|
UAGAUAA
|
chr4
|
|
na
|
na
|
|
Hsa-Mir-147_3p
|
-5.82
|
0.43
|
5.54e-40
|
2
|
110
|
hsa-mir-147b
|
MIR-147
|
UGUGCGG
|
chr15
|
|
na
|
na
|
|
Hsa-Mir-190-P1_5p
|
-4.03
|
0.32
|
1.35e-34
|
22
|
294
|
hsa-mir-190a
|
MIR-190
|
GAUAUGU
|
chr15
|
|
na
|
na
|
|
Hsa-Mir-378_3p
|
-3.17
|
0.24
|
2.10e-38
|
2189
|
14834
|
hsa-mir-378a
|
MIR-378
|
CUGGACU
|
chr5
|
|
na
|
na
|
|
Hsa-Mir-127_3p
|
-2.96
|
0.40
|
4.55e-12
|
327
|
1967
|
hsa-mir-127
|
MIR-127
|
CGGAUCC
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-10-P1b_5p
|
-2.67
|
0.45
|
5.72e-09
|
14125
|
75067
|
hsa-mir-10b
|
MIR-10
|
ACCCUGU
|
chr2
|
|
na
|
na
|
|
Hsa-Mir-8-P1a_3p
|
-2.66
|
0.29
|
3.55e-18
|
353
|
1754
|
hsa-mir-200a
|
MIR-8
|
AACACUG
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-8-P3a_3p
|
-2.65
|
0.30
|
1.86e-17
|
262
|
1282
|
hsa-mir-429
|
MIR-8
|
AAUACUG
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-154-P23_3p
|
-1.79
|
0.25
|
3.73e-11
|
41
|
108
|
hsa-mir-654
|
MIR-154
|
AUGUCUG
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-425_5p
|
-1.66
|
0.23
|
3.12e-11
|
213
|
499
|
hsa-mir-425
|
MIR-425
|
AUGACAC
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-154-P9_3p
|
-1.37
|
0.26
|
6.98e-07
|
135
|
265
|
hsa-mir-381
|
MIR-154
|
AUACAAG
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-154-P13_5p
|
-1.35
|
0.27
|
3.42e-06
|
161
|
311
|
hsa-mir-411
|
MIR-154
|
AGUAGAC
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-28-P1_3p
|
-1.24
|
0.23
|
3.62e-07
|
2385
|
3956
|
hsa-mir-28
|
MIR-28
|
ACUAGAU
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-17-P3a_5p
|
-1.23
|
0.29
|
2.53e-04
|
194
|
322
|
hsa-mir-20a
|
MIR-17
|
AAAGUGC
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-1307_3p
|
-1.09
|
0.24
|
2.47e-05
|
80
|
127
|
hsa-mir-1307
|
MIR-1307
|
CGACCGG
|
chr10
|
|
na
|
na
|
|
Hsa-Mir-8-P1b_3p
|
-1.06
|
0.38
|
1.07e-02
|
6145
|
9180
|
hsa-mir-141
|
MIR-8
|
AACACUG
|
chr12
|
|
na
|
na
|
|
Hsa-Mir-1307_5p
|
-1.03
|
0.32
|
4.10e-03
|
444
|
713
|
hsa-mir-1307
|
MIR-1307
|
CGACCGG
|
chr10
|
|
na
|
na
|
|
Hsa-Mir-362-P3_3p
|
-1.00
|
0.45
|
4.09e-02
|
82
|
103
|
hsa-mir-501
|
MIR-362
|
AUGCACC
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-210_3p
|
-0.93
|
0.35
|
3.36e-02
|
138
|
185
|
hsa-mir-210
|
MIR-210
|
UGUGCGU
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-136_3p
|
-0.89
|
0.27
|
2.95e-03
|
88
|
127
|
hsa-mir-136
|
MIR-136
|
AUCAUCG
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-21_5p
|
-0.86
|
0.22
|
6.21e-04
|
21349
|
28173
|
hsa-mir-21
|
MIR-21
|
AGCUUAU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-30-P1b_5p
|
-0.86
|
0.18
|
8.68e-06
|
4513
|
6046
|
hsa-mir-30e
|
MIR-30
|
GUAAACA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-191_5p
|
-0.82
|
0.28
|
1.02e-02
|
12597
|
14608
|
hsa-mir-191
|
MIR-191
|
AACGGAA
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-148-P1_3p
|
-0.72
|
0.30
|
4.78e-02
|
18874
|
21730
|
hsa-mir-148a
|
MIR-148
|
CAGUGCA
|
chr7
|
|
na
|
na
|
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 35
nCR vs pCRC
column='tissue.type'
tissue_type_A <- 'tumor.colorect'
tissue_type_B <- 'normal.colorect'
norm_adj_up = "None"
norm_adj_down = "None"
pCRC_adj_up = "None"
pCRC_adj_down = "None"
coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
lfc.Threshold, rpm.Threshold,
norm_adj_up,
norm_adj_down)
dict_sig_mirna[paste(coef, "up", sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
tissue_type_A, tissue_type_B,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_tumor.colorect_vs_normal.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM tumor.colorect
|
RPM normal.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-17-P1a_5p/P1b_5p
|
1.42
|
0.14
|
1.30e-22
|
900
|
225
|
hsa-mir-17
|
MIR-17
|
AAAGUGC
|
chr13
|
CD14+ Monocyte
|
na
|
na
|
|
Hsa-Mir-7-P1_5p/P2_5p/P3_5p
|
2.32
|
0.25
|
8.96e-18
|
199
|
22
|
hsa-mir-7-1
|
MIR-7
|
GGAAGAC
|
chr9
|
c(“Islet Cell”, “Neural”)
|
na
|
na
|
|
Hsa-Mir-223_3p
|
1.22
|
0.24
|
2.50e-06
|
619
|
177
|
hsa-mir-223
|
MIR-223
|
GUCAGUU
|
chrX
|
c(“Dendritic Cell”, “Macrophage”)
|
na
|
na
|
|
Hsa-Mir-31_5p
|
4.32
|
0.33
|
1.40e-44
|
627
|
6
|
hsa-mir-31
|
MIR-31
|
GGCAAGA
|
chr9
|
|
na
|
na
|
|
Hsa-Mir-135-P3_5p
|
4.10
|
0.23
|
1.68e-69
|
155
|
4
|
hsa-mir-135b
|
MIR-135
|
AUGGCUU
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-224_5p
|
2.48
|
0.19
|
2.72e-36
|
395
|
43
|
hsa-mir-224
|
MIR-224
|
AAGUCAC
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-584_5p
|
1.94
|
0.21
|
2.16e-19
|
110
|
17
|
hsa-mir-584
|
MIR-584
|
UAUGGUU
|
chr5
|
|
na
|
na
|
|
Hsa-Mir-15-P1d_5p
|
1.87
|
0.21
|
1.15e-17
|
235
|
40
|
hsa-mir-424
|
MIR-15
|
AGCAGCA
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-96-P2_5p
|
1.84
|
0.17
|
6.44e-24
|
10417
|
1867
|
hsa-mir-182
|
MIR-96
|
UUGGCAA
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-17-P3a_5p
|
1.62
|
0.16
|
2.85e-21
|
1513
|
322
|
hsa-mir-20a
|
MIR-17
|
AAAGUGC
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-96-P3_5p
|
1.53
|
0.23
|
1.96e-10
|
1063
|
224
|
hsa-mir-183
|
MIR-96
|
AUGGCAC
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-19-P1_3p
|
1.47
|
0.17
|
1.43e-17
|
379
|
87
|
hsa-mir-19a
|
MIR-19
|
GUGCAAA
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-21_5p
|
1.35
|
0.13
|
1.01e-24
|
105735
|
28173
|
hsa-mir-21
|
MIR-21
|
AGCUUAU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-17-P2a_5p
|
1.33
|
0.19
|
6.62e-11
|
168
|
44
|
hsa-mir-18a
|
MIR-17
|
AAGGUGC
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-181-P2c_5p
|
1.33
|
0.16
|
1.40e-15
|
196
|
51
|
hsa-mir-181d
|
MIR-181
|
ACAUUCA
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-95-P2_3p
|
1.17
|
0.14
|
1.40e-15
|
142
|
41
|
hsa-mir-421
|
MIR-95
|
UCAACAG
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-19-P2a_3p/P2b_3p
|
1.14
|
0.16
|
1.59e-12
|
1297
|
389
|
hsa-mir-19b-1
|
MIR-19
|
GUGCAAA
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-181-P1c_5p
|
1.11
|
0.15
|
1.06e-12
|
1724
|
527
|
hsa-mir-181c
|
MIR-181
|
ACAUUCA
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-29-P2a_3p/P2b_3p
|
1.08
|
0.17
|
5.43e-10
|
380
|
124
|
hsa-mir-29b-1
|
MIR-29
|
AGCACCA
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-130-P2a_3p
|
1.05
|
0.15
|
8.09e-12
|
409
|
131
|
hsa-mir-301a
|
MIR-130
|
AGUGCAA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-17-P3c_5p
|
0.97
|
0.12
|
1.00e-15
|
370
|
130
|
hsa-mir-106b
|
MIR-17
|
AAAGUGC
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-221-P2_3p
|
0.94
|
0.15
|
1.32e-09
|
1633
|
574
|
hsa-mir-222
|
MIR-221
|
GCUACAU
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-92-P1a_3p/P1b_3p
|
0.90
|
0.16
|
5.66e-08
|
39320
|
13849
|
hsa-mir-92a-1
|
MIR-92
|
AUUGCAC
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-17-P1c_5p
|
0.88
|
0.10
|
2.69e-17
|
2287
|
843
|
hsa-mir-93
|
MIR-17
|
AAAGUGC
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-221-P1_3p
|
0.83
|
0.13
|
4.23e-09
|
2529
|
964
|
hsa-mir-221
|
MIR-221
|
GCUACAU
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-203_3p
|
0.74
|
0.20
|
9.13e-04
|
2307
|
922
|
hsa-mir-203a
|
MIR-203
|
UGAAAUG
|
chr14
|
|
na
|
na
|
|
Hsa-Let-7-P2c2_5p
|
0.74
|
0.12
|
1.50e-08
|
5432
|
2218
|
hsa-let-7i
|
LET-7
|
GAGGUAG
|
chr12
|
|
na
|
na
|
|
Hsa-Let-7-P2b3_5p
|
0.68
|
0.14
|
4.33e-06
|
2149
|
937
|
hsa-mir-98
|
LET-7
|
GAGGUAG
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-29-P1a_3p
|
0.62
|
0.12
|
1.40e-06
|
3667
|
1692
|
hsa-mir-29a
|
MIR-29
|
AGCACCA
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-92-P2c_3p
|
0.62
|
0.10
|
4.70e-09
|
3274
|
1443
|
hsa-mir-25
|
MIR-92
|
AUUGCAC
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-92-P1c_3p
|
0.60
|
0.18
|
4.90e-03
|
1398
|
634
|
hsa-mir-92b
|
MIR-92
|
AUUGCAC
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-769_5p
|
0.59
|
0.11
|
1.18e-06
|
439
|
195
|
hsa-mir-769
|
MIR-769
|
GAGACCU
|
chr19
|
|
na
|
na
|
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 32
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_tumor.colorect_vs_normal.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM tumor.colorect
|
RPM normal.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-451_5p
|
-1.19
|
0.26
|
3.69e-05
|
1565
|
2815
|
hsa-mir-451a
|
MIR-451
|
AACCGUU
|
chr17
|
Red Blood Cell
|
na
|
na
|
|
Hsa-Mir-145_5p
|
-1.97
|
0.22
|
1.63e-17
|
840
|
2832
|
hsa-mir-145
|
MIR-145
|
UCCAGUU
|
chr5
|
Mesenchymal
|
na
|
na
|
|
Hsa-Mir-143_3p
|
-0.60
|
0.19
|
1.39e-03
|
148431
|
164812
|
hsa-mir-143
|
MIR-143
|
GAGAUGA
|
chr5
|
Mesenchymal
|
na
|
na
|
|
Hsa-Mir-150_5p
|
-1.36
|
0.24
|
3.58e-07
|
280
|
586
|
hsa-mir-150
|
MIR-150
|
CUCCCAA
|
chr19
|
Lymphocyte
|
na
|
na
|
|
Hsa-Mir-192-P1_5p/P2_5p
|
-2.05
|
0.22
|
3.04e-19
|
13717
|
46010
|
hsa-mir-192
|
MIR-192
|
UGACCUA
|
chr11
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-133-P1_3p/P2_3p/P3_3p
|
-1.98
|
0.26
|
6.98e-16
|
116
|
460
|
hsa-mir-133a-2
|
MIR-133
|
UUGGUCC
|
chr20
|
c(“Skeletal Myocyte”, “Stem Cell”)
|
na
|
na
|
|
Hsa-Mir-486_5p
|
-1.18
|
0.25
|
2.02e-05
|
2013
|
3961
|
hsa-mir-486-1
|
MIR-486
|
CCUGUAC
|
chr8
|
c(“Platelet”, “Red Blood Cell”)
|
na
|
na
|
|
Hsa-Mir-375_3p
|
-1.86
|
0.26
|
2.92e-12
|
5263
|
17215
|
hsa-mir-375
|
MIR-375
|
UUGUUCG
|
chr2
|
c(“Epithelial Cell”, “Islet Cell”, “Neural”)
|
na
|
na
|
|
Hsa-Mir-126_5p
|
-0.72
|
0.15
|
1.61e-05
|
3455
|
4230
|
hsa-mir-126
|
MIR-126
|
AUUAUUA
|
chr9
|
c(“Endothelial Cell”, “Platelet”)
|
na
|
na
|
|
Hsa-Mir-342_3p
|
-1.26
|
0.18
|
1.59e-10
|
145
|
260
|
hsa-mir-342
|
MIR-342
|
CUCACAC
|
chr14
|
c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”)
|
na
|
na
|
|
Hsa-Mir-147_3p
|
-1.92
|
0.23
|
8.04e-17
|
33
|
110
|
hsa-mir-147b
|
MIR-147
|
UGUGCGG
|
chr15
|
|
na
|
na
|
|
Hsa-Mir-15-P2c_5p
|
-1.76
|
0.17
|
6.42e-23
|
257
|
672
|
hsa-mir-195
|
MIR-15
|
AGCAGCA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-378_3p
|
-1.72
|
0.14
|
8.99e-34
|
6426
|
14834
|
hsa-mir-378a
|
MIR-378
|
CUGGACU
|
chr5
|
|
na
|
na
|
|
Hsa-Mir-15-P1c_5p
|
-1.62
|
0.16
|
5.86e-22
|
143
|
337
|
hsa-mir-497
|
MIR-15
|
AGCAGCA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-190-P1_5p
|
-1.47
|
0.18
|
1.43e-15
|
140
|
294
|
hsa-mir-190a
|
MIR-190
|
GAUAUGU
|
chr15
|
|
na
|
na
|
|
Hsa-Mir-194-P1_5p/P2_5p
|
-1.31
|
0.19
|
3.24e-11
|
6167
|
11619
|
hsa-mir-194-2
|
MIR-194
|
GUAACAG
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-30-P1a_5p
|
-1.21
|
0.16
|
7.30e-12
|
3165
|
5150
|
hsa-mir-30a
|
MIR-30
|
GUAAACA
|
chr6
|
|
na
|
na
|
|
Hsa-Mir-26-P3_5p
|
-1.21
|
0.14
|
3.12e-16
|
3467
|
5898
|
hsa-mir-26a-1
|
MIR-26
|
UCAAGUA
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-338-P1_3p
|
-1.18
|
0.22
|
8.59e-07
|
98
|
166
|
hsa-mir-338
|
MIR-338
|
CCAGCAU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-26-P1_5p/P2_5p
|
-1.15
|
0.10
|
4.50e-29
|
35584
|
56268
|
hsa-mir-26b
|
MIR-26
|
UCAAGUA
|
chr2
|
|
na
|
na
|
|
Hsa-Mir-10-P1b_5p
|
-1.11
|
0.25
|
4.63e-06
|
39286
|
75067
|
hsa-mir-10b
|
MIR-10
|
ACCCUGU
|
chr2
|
|
na
|
na
|
|
Hsa-Mir-192-P1_5p
|
-1.11
|
0.20
|
2.37e-08
|
132000
|
212293
|
hsa-mir-192
|
MIR-192
|
UGACCUA
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-148-P2_3p
|
-1.04
|
0.21
|
5.54e-06
|
137
|
210
|
hsa-mir-152
|
MIR-148
|
CAGUGCA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-15-P2a_5p/P2b_5p
|
-1.02
|
0.12
|
8.21e-15
|
4522
|
6926
|
hsa-mir-16-1
|
MIR-15
|
AGCAGCA
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-10-P3b_5p
|
-0.97
|
0.19
|
4.09e-06
|
1649
|
2392
|
hsa-mir-125a
|
MIR-10
|
CCCUGAG
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-28-P1_3p
|
-0.95
|
0.13
|
6.44e-12
|
2915
|
3956
|
hsa-mir-28
|
MIR-28
|
ACUAGAU
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-1307_5p
|
-0.95
|
0.18
|
2.27e-06
|
492
|
713
|
hsa-mir-1307
|
MIR-1307
|
CGACCGG
|
chr10
|
|
na
|
na
|
|
Hsa-Mir-29-P1b_3p
|
-0.93
|
0.15
|
1.11e-09
|
331
|
464
|
hsa-mir-29c
|
MIR-29
|
AGCACCA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-10-P3c_5p
|
-0.80
|
0.21
|
1.44e-03
|
239
|
326
|
hsa-mir-125b-2
|
MIR-10
|
CCCUGAG
|
chr21
|
|
na
|
na
|
|
Hsa-Mir-574_3p
|
-0.78
|
0.14
|
1.83e-07
|
240
|
305
|
hsa-mir-574
|
MIR-574
|
ACGCUCA
|
chr4
|
|
na
|
na
|
|
Hsa-Mir-8-P1b_3p
|
-0.69
|
0.21
|
2.87e-03
|
7518
|
9180
|
hsa-mir-141
|
MIR-8
|
AACACUG
|
chr12
|
|
na
|
na
|
|
Hsa-Mir-191_5p
|
-0.68
|
0.16
|
1.46e-04
|
13630
|
14608
|
hsa-mir-191
|
MIR-191
|
AACGGAA
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-362-P3_3p
|
-0.62
|
0.25
|
1.92e-02
|
108
|
103
|
hsa-mir-501
|
MIR-362
|
AUGCACC
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-142_5p
|
-0.60
|
0.19
|
4.90e-03
|
3453
|
3817
|
hsa-mir-142
|
MIR-142
|
AUAAAGU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-154-P9_3p
|
-0.59
|
0.15
|
1.81e-04
|
253
|
265
|
hsa-mir-381
|
MIR-154
|
AUACAAG
|
chr14
|
|
na
|
na
|
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 35
ref <- 'tumor.colorect'
dds <- DeseqObject(design, countdata, sampleinfo, "None", "None", ref)
#
# #datasets in total
dim(dds[, colData(dds)$type.tissue == 'pCRC'])
## [1] 389 120
dim(dds[, colData(dds)$type.tissue == 'mLi'])
## [1] 389 35
dim(dds[, colData(dds)$type.tissue == 'mLu'])
## [1] 389 28
dim(dds[, colData(dds)$type.tissue == 'nCR'])
## [1] 389 25
dim(dds[, colData(dds)$type.tissue == 'nLi'])
## [1] 389 20
dim(dds[, colData(dds)$type.tissue == 'nLu'])
## [1] 389 10
dim(dds[, colData(dds)$type.tissue == 'PM'])
## [1] 389 30
# #datasets for Fromm
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'fromm'])
## [1] 389 3
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'fromm'])
## [1] 389 19
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'fromm'])
## [1] 389 24
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'fromm'])
## [1] 389 3
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'fromm'])
## [1] 389 8
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'fromm'])
## [1] 389 7
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'fromm'])
## [1] 389 18
# #datasets for Schee
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'schee'])
## [1] 389 83
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'schee'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'schee'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'schee'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'schee'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'schee'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'schee'])
## [1] 389 0
# #datasets for Schee
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'neerincx'])
## [1] 389 34
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'neerincx'])
## [1] 389 16
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'neerincx'])
## [1] 389 4
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'neerincx'])
## [1] 389 22
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'neerincx'])
## [1] 389 9
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'neerincx'])
## [1] 389 3
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'neerincx'])
## [1] 389 12
# #datasets for Schee
dim(dds[, colData(dds)$type.tissue == 'pCRC' & colData(dds)$paper == 'selitsky'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'mLi' & colData(dds)$paper == 'selitsky'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'mLu' & colData(dds)$paper == 'selitsky'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'nCR' & colData(dds)$paper == 'selitsky'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'nLi' & colData(dds)$paper == 'selitsky'])
## [1] 389 3
dim(dds[, colData(dds)$type.tissue == 'nLu' & colData(dds)$paper == 'selitsky'])
## [1] 389 0
dim(dds[, colData(dds)$type.tissue == 'PM' & colData(dds)$paper == 'selitsky'])
## [1] 389 0
plotDispEsts(dds)
## pCRC vs nLi
column='tissue.type'
tissue_type_A <- 'normal.liver'
tissue_type_B <- 'tumor.colorect'
norm_adj_up = "None"
norm_adj_down = "None"
pCRC_adj_up = "None"
pCRC_adj_down = "None"
coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
lfc.Threshold, rpm.Threshold,
norm_adj_up,
norm_adj_down)
dict_sig_mirna[paste(coef, "up", sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
tissue_type_A, tissue_type_B,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_normal.liver_vs_tumor.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM normal.liver
|
RPM tumor.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-204-P1_5p
|
1.03
|
0.46
|
1.25e-04
|
190
|
62
|
hsa-mir-204
|
MIR-204
|
UCCCUUU
|
chr9
|
Retinal Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-335_5p
|
0.81
|
0.14
|
1.89e-08
|
205
|
150
|
hsa-mir-335
|
MIR-335
|
CAAGAGC
|
chr7
|
Retinal Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-144_5p
|
2.22
|
0.28
|
3.68e-15
|
206
|
57
|
hsa-mir-144
|
MIR-144
|
GAUAUCA
|
chr17
|
Red Blood Cell
|
na
|
na
|
|
Hsa-Mir-451_5p
|
1.48
|
0.32
|
7.08e-06
|
3358
|
1565
|
hsa-mir-451a
|
MIR-451
|
AACCGUU
|
chr17
|
Red Blood Cell
|
na
|
na
|
|
Hsa-Mir-150_5p
|
2.07
|
0.29
|
5.24e-13
|
1053
|
280
|
hsa-mir-150
|
MIR-150
|
CUCCCAA
|
chr19
|
Lymphocyte
|
na
|
na
|
|
Hsa-Mir-122_5p
|
11.53
|
0.78
|
4.04e-55
|
148842
|
9
|
hsa-mir-122
|
MIR-122
|
GGAGUGU
|
chr18
|
Hepatocyte
|
na
|
na
|
|
Hsa-Mir-192-P1_5p/P2_5p
|
1.31
|
0.27
|
2.62e-06
|
29546
|
13717
|
hsa-mir-192
|
MIR-192
|
UGACCUA
|
chr11
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-15-P1a_5p
|
0.62
|
0.12
|
1.17e-06
|
555
|
453
|
hsa-mir-15a
|
MIR-15
|
AGCAGCA
|
chr13
|
CD14+ Monocyte
|
na
|
na
|
|
Hsa-Mir-486_5p
|
2.48
|
0.30
|
8.95e-16
|
10617
|
2013
|
hsa-mir-486-1
|
MIR-486
|
CCUGUAC
|
chr8
|
c(“Platelet”, “Red Blood Cell”)
|
na
|
na
|
|
Hsa-Mir-126_5p
|
1.95
|
0.17
|
7.32e-30
|
11148
|
3455
|
hsa-mir-126
|
MIR-126
|
AUUAUUA
|
chr9
|
c(“Endothelial Cell”, “Platelet”)
|
na
|
na
|
|
Hsa-Mir-342_3p
|
1.05
|
0.21
|
5.60e-07
|
254
|
145
|
hsa-mir-342
|
MIR-342
|
CUCACAC
|
chr14
|
c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”)
|
na
|
na
|
|
Hsa-Mir-885_5p
|
9.48
|
0.68
|
8.47e-41
|
510
|
0
|
hsa-mir-885
|
MIR-885
|
CCAUUAC
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-139_5p
|
4.20
|
0.21
|
6.49e-84
|
174
|
11
|
hsa-mir-139
|
MIR-139
|
CUACAGU
|
chr11
|
|
na
|
na
|
|
Hsa-Let-7-P1c_5p
|
3.25
|
0.23
|
3.14e-43
|
4945
|
606
|
hsa-let-7c
|
LET-7
|
GAGGUAG
|
chr21
|
|
na
|
na
|
|
Hsa-Mir-10-P2c_5p
|
3.24
|
0.28
|
2.36e-31
|
1202
|
142
|
hsa-mir-99a
|
MIR-10
|
ACCCGUA
|
chr21
|
|
na
|
na
|
|
Hsa-Mir-10-P3c_5p
|
2.87
|
0.25
|
2.44e-30
|
1620
|
239
|
hsa-mir-125b-2
|
MIR-10
|
CCCUGAG
|
chr21
|
|
na
|
na
|
|
Hsa-Mir-30-P1a_5p
|
2.51
|
0.19
|
3.24e-40
|
15175
|
3165
|
hsa-mir-30a
|
MIR-30
|
GUAAACA
|
chr6
|
|
na
|
na
|
|
Hsa-Mir-193-P1a_5p
|
2.48
|
0.23
|
3.82e-25
|
298
|
65
|
hsa-mir-193a
|
MIR-193
|
GGGUCUU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-193-P2a_3p/P2b_3p
|
2.33
|
0.19
|
1.61e-32
|
219
|
56
|
hsa-mir-365b
|
MIR-193
|
AAUGCCC
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-10-P3a_5p
|
2.29
|
0.26
|
3.08e-18
|
611
|
145
|
hsa-mir-125b-1
|
MIR-10
|
CCCUGAG
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-193-P1b_3p
|
2.17
|
0.19
|
2.30e-29
|
514
|
145
|
hsa-mir-193b
|
MIR-193
|
ACUGGCC
|
chr16
|
|
na
|
na
|
|
Hsa-Mir-455_5p
|
2.10
|
0.15
|
6.57e-45
|
279
|
83
|
hsa-mir-455
|
MIR-455
|
AUGUGCC
|
chr9
|
|
na
|
na
|
|
Hsa-Mir-574_3p
|
1.99
|
0.16
|
1.44e-34
|
745
|
240
|
hsa-mir-574
|
MIR-574
|
ACGCUCA
|
chr4
|
|
na
|
na
|
|
Hsa-Mir-101-P1_3p/P2_3p
|
1.98
|
0.15
|
6.40e-41
|
14649
|
4638
|
hsa-mir-101-1
|
MIR-101
|
UACAGUA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-148-P1_3p
|
1.86
|
0.20
|
1.75e-19
|
105086
|
34704
|
hsa-mir-148a
|
MIR-148
|
CAGUGCA
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-423_5p
|
1.83
|
0.20
|
1.11e-18
|
1198
|
433
|
hsa-mir-423
|
MIR-423
|
GAGGGGC
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-22-P1a_3p
|
1.79
|
0.14
|
1.08e-36
|
78932
|
29018
|
hsa-mir-22
|
MIR-22
|
AGCUGCC
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-744_5p
|
1.79
|
0.17
|
1.28e-23
|
165
|
61
|
hsa-mir-744
|
MIR-744
|
GCGGGGC
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-30-P2a_5p/P2b_5p/P2c_5p
|
1.45
|
0.12
|
7.16e-32
|
5891
|
2793
|
hsa-mir-30c-2
|
MIR-30
|
GUAAACA
|
chr6
|
|
na
|
na
|
|
Hsa-Mir-130-P1a_3p
|
1.43
|
0.16
|
1.39e-17
|
907
|
421
|
hsa-mir-130a
|
MIR-130
|
AGUGCAA
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-378_3p
|
1.35
|
0.16
|
1.07e-16
|
12736
|
6426
|
hsa-mir-378a
|
MIR-378
|
CUGGACU
|
chr5
|
|
na
|
na
|
|
Hsa-Mir-148-P2_3p
|
1.34
|
0.25
|
1.57e-07
|
289
|
137
|
hsa-mir-152
|
MIR-148
|
CAGUGCA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-197_3p
|
1.28
|
0.15
|
5.68e-16
|
344
|
181
|
hsa-mir-197
|
MIR-197
|
UCACCAC
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-15-P1c_5p
|
1.25
|
0.19
|
3.90e-11
|
276
|
143
|
hsa-mir-497
|
MIR-15
|
AGCAGCA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-26-P3_5p
|
1.24
|
0.16
|
2.22e-14
|
6787
|
3467
|
hsa-mir-26a-1
|
MIR-26
|
UCAAGUA
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-15-P2c_5p
|
1.19
|
0.20
|
1.78e-09
|
496
|
257
|
hsa-mir-195
|
MIR-15
|
AGCAGCA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-331_3p
|
1.16
|
0.21
|
6.75e-08
|
110
|
60
|
hsa-mir-331
|
MIR-331
|
CCCCUGG
|
chr12
|
|
na
|
na
|
|
Hsa-Mir-26-P1_5p/P2_5p
|
1.10
|
0.11
|
1.13e-22
|
62274
|
35584
|
hsa-mir-26b
|
MIR-26
|
UCAAGUA
|
chr2
|
|
na
|
na
|
|
Hsa-Mir-10-P3b_5p
|
1.09
|
0.23
|
2.99e-06
|
3040
|
1649
|
hsa-mir-125a
|
MIR-10
|
CCCUGAG
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-30-P1b_5p
|
1.08
|
0.12
|
5.44e-19
|
11240
|
6833
|
hsa-mir-30e
|
MIR-30
|
GUAAACA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-340_5p
|
1.05
|
0.13
|
1.20e-15
|
1030
|
657
|
hsa-mir-340
|
MIR-340
|
UAUAAAG
|
chr5
|
|
na
|
na
|
|
Hsa-Mir-27-P1_3p/P2_3p
|
1.04
|
0.12
|
4.13e-17
|
50009
|
29444
|
hsa-mir-27a
|
MIR-27
|
UCACAGU
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-10-P2a_5p
|
1.00
|
0.27
|
4.80e-04
|
2634
|
1724
|
hsa-mir-100
|
MIR-10
|
ACCCGUA
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-29-P1b_3p
|
0.98
|
0.17
|
1.55e-08
|
516
|
331
|
hsa-mir-29c
|
MIR-29
|
AGCACCA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-154-P23_3p
|
0.94
|
0.16
|
3.85e-08
|
222
|
148
|
hsa-mir-654
|
MIR-154
|
AUGUCUG
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-15-P2a_5p/P2b_5p
|
0.87
|
0.14
|
9.10e-10
|
6884
|
4522
|
hsa-mir-16-1
|
MIR-15
|
AGCAGCA
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-30-P1c_5p
|
0.84
|
0.13
|
9.49e-10
|
13680
|
9703
|
hsa-mir-30d
|
MIR-30
|
GUAAACA
|
chr8
|
|
na
|
na
|
|
Hsa-Mir-154-P13_5p
|
0.78
|
0.18
|
2.94e-05
|
442
|
328
|
hsa-mir-411
|
MIR-154
|
AGUAGAC
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-28-P2_5p
|
0.76
|
0.15
|
1.01e-06
|
3098
|
2244
|
hsa-mir-151a
|
MIR-28
|
CGAGGAG
|
chr8
|
|
na
|
na
|
|
Hsa-Mir-92-P1a_3p/P1b_3p
|
0.71
|
0.18
|
2.33e-04
|
51173
|
39320
|
hsa-mir-92a-1
|
MIR-92
|
AUUGCAC
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-136_3p
|
0.65
|
0.18
|
4.46e-04
|
208
|
173
|
hsa-mir-136
|
MIR-136
|
AUCAUCG
|
chr14
|
|
na
|
na
|
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 51
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_normal.liver_vs_tumor.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM normal.liver
|
RPM tumor.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-143_3p
|
-1.72
|
0.23
|
4.38e-14
|
37572
|
148431
|
hsa-mir-143
|
MIR-143
|
GAGAUGA
|
chr5
|
Mesenchymal
|
na
|
na
|
|
Hsa-Mir-24-P1_3p/P2_3p
|
-0.77
|
0.16
|
8.36e-06
|
319
|
626
|
hsa-mir-24-2
|
MIR-24
|
GGCUCAG
|
chr19
|
Macrophage
|
na
|
na
|
|
Hsa-Mir-8-P2b_3p
|
-6.07
|
0.19
|
3.90e-210
|
31
|
2727
|
hsa-mir-200c
|
MIR-8
|
AAUACUG
|
chr12
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-8-P2a_3p
|
-4.36
|
0.19
|
2.71e-109
|
369
|
10330
|
hsa-mir-200b
|
MIR-8
|
AAUACUG
|
chr1
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-17-P1a_5p/P1b_5p
|
-1.32
|
0.16
|
4.02e-16
|
278
|
900
|
hsa-mir-17
|
MIR-17
|
AAAGUGC
|
chr13
|
CD14+ Monocyte
|
na
|
na
|
|
Hsa-Mir-15-P1b_5p
|
-0.92
|
0.13
|
1.30e-11
|
114
|
273
|
hsa-mir-15b
|
MIR-15
|
AGCAGCA
|
chr3
|
CD14+ Monocyte
|
na
|
na
|
|
Hsa-Mir-133-P1_3p/P2_3p/P3_3p
|
-2.03
|
0.32
|
1.91e-10
|
22
|
116
|
hsa-mir-133a-2
|
MIR-133
|
UUGGUCC
|
chr20
|
c(“Skeletal Myocyte”, “Stem Cell”)
|
na
|
na
|
|
Hsa-Mir-155_5p
|
-1.44
|
0.21
|
1.42e-10
|
113
|
387
|
hsa-mir-155
|
MIR-155
|
UAAUGCU
|
chr21
|
c(“Lymphocyte”, “Macrophage”)
|
na
|
na
|
|
Hsa-Mir-7-P1_5p/P2_5p/P3_5p
|
-5.51
|
0.32
|
2.03e-63
|
3
|
199
|
hsa-mir-7-1
|
MIR-7
|
GGAAGAC
|
chr9
|
c(“Islet Cell”, “Neural”)
|
na
|
na
|
|
Hsa-Mir-375_3p
|
-0.84
|
0.32
|
2.21e-02
|
2553
|
5263
|
hsa-mir-375
|
MIR-375
|
UUGUUCG
|
chr2
|
c(“Epithelial Cell”, “Islet Cell”, “Neural”)
|
na
|
na
|
|
Hsa-Mir-196-P1_5p/P2_5p
|
-6.71
|
0.30
|
2.31e-108
|
2
|
319
|
hsa-mir-196a-1
|
MIR-196
|
AGGUAGU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-135-P3_5p
|
-6.12
|
0.31
|
3.71e-82
|
2
|
155
|
hsa-mir-135b
|
MIR-135
|
AUGGCUU
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-196-P3_5p
|
-6.11
|
0.32
|
1.17e-77
|
9
|
828
|
hsa-mir-196b
|
MIR-196
|
AGGUAGU
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-31_5p
|
-5.76
|
0.45
|
3.20e-37
|
7
|
627
|
hsa-mir-31
|
MIR-31
|
GGCAAGA
|
chr9
|
|
na
|
na
|
|
Hsa-Mir-577_5p
|
-5.70
|
0.29
|
1.37e-80
|
2
|
142
|
hsa-mir-577
|
MIR-577
|
UAGAUAA
|
chr4
|
|
na
|
na
|
|
Hsa-Mir-8-P1b_3p
|
-5.35
|
0.25
|
1.29e-93
|
154
|
7518
|
hsa-mir-141
|
MIR-8
|
AACACUG
|
chr12
|
|
na
|
na
|
|
Hsa-Mir-96-P3_5p
|
-5.08
|
0.27
|
2.84e-74
|
27
|
1063
|
hsa-mir-183
|
MIR-96
|
AUGGCAC
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-8-P3a_3p
|
-4.60
|
0.20
|
5.55e-118
|
68
|
2303
|
hsa-mir-429
|
MIR-8
|
AAUACUG
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-96-P2_5p
|
-4.47
|
0.20
|
4.46e-107
|
370
|
10417
|
hsa-mir-182
|
MIR-96
|
UUGGCAA
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-10-P1b_5p
|
-3.68
|
0.31
|
1.37e-31
|
2704
|
39286
|
hsa-mir-10b
|
MIR-10
|
ACCCUGU
|
chr2
|
|
na
|
na
|
|
Hsa-Mir-8-P1a_3p
|
-3.64
|
0.19
|
9.42e-76
|
108
|
1772
|
hsa-mir-200a
|
MIR-8
|
AACACUG
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-203_3p
|
-3.42
|
0.24
|
2.77e-45
|
171
|
2307
|
hsa-mir-203a
|
MIR-203
|
UGAAAUG
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-221-P1_3p
|
-3.13
|
0.15
|
5.83e-92
|
217
|
2529
|
hsa-mir-221
|
MIR-221
|
GCUACAU
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-224_5p
|
-2.99
|
0.22
|
2.60e-39
|
36
|
395
|
hsa-mir-224
|
MIR-224
|
AAGUCAC
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-221-P2_3p
|
-2.81
|
0.17
|
1.32e-61
|
173
|
1633
|
hsa-mir-222
|
MIR-221
|
GCUACAU
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-181-P2c_5p
|
-2.59
|
0.19
|
7.58e-42
|
26
|
196
|
hsa-mir-181d
|
MIR-181
|
ACAUUCA
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-181-P1c_5p
|
-2.35
|
0.17
|
3.48e-42
|
261
|
1724
|
hsa-mir-181c
|
MIR-181
|
ACAUUCA
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-584_5p
|
-2.25
|
0.25
|
4.29e-18
|
18
|
110
|
hsa-mir-584
|
MIR-584
|
UAUGGUU
|
chr5
|
|
na
|
na
|
|
Hsa-Mir-190-P1_5p
|
-2.24
|
0.22
|
4.84e-24
|
23
|
140
|
hsa-mir-190a
|
MIR-190
|
GAUAUGU
|
chr15
|
|
na
|
na
|
|
Hsa-Mir-17-P2a_5p
|
-2.22
|
0.23
|
1.80e-21
|
26
|
168
|
hsa-mir-18a
|
MIR-17
|
AAGGUGC
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-21_5p
|
-2.10
|
0.14
|
5.68e-48
|
19240
|
105735
|
hsa-mir-21
|
MIR-21
|
AGCUUAU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-92-P1c_3p
|
-1.90
|
0.21
|
3.08e-18
|
303
|
1398
|
hsa-mir-92b
|
MIR-92
|
AUUGCAC
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-10-P1a_5p
|
-1.79
|
0.25
|
2.08e-11
|
25828
|
97123
|
hsa-mir-10a
|
MIR-10
|
ACCCUGU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-146-P1_5p
|
-1.77
|
0.29
|
4.42e-09
|
351
|
1453
|
hsa-mir-146a
|
MIR-146
|
GAGAACU
|
chr5
|
|
na
|
na
|
|
Hsa-Mir-130-P2a_3p
|
-1.75
|
0.17
|
1.02e-24
|
92
|
409
|
hsa-mir-301a
|
MIR-130
|
AGUGCAA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-29-P2a_3p/P2b_3p
|
-1.48
|
0.19
|
9.97e-14
|
107
|
380
|
hsa-mir-29b-1
|
MIR-29
|
AGCACCA
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-17-P3c_5p
|
-1.47
|
0.13
|
8.74e-29
|
100
|
370
|
hsa-mir-106b
|
MIR-17
|
AAAGUGC
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-17-P1c_5p
|
-1.45
|
0.11
|
7.11e-37
|
647
|
2287
|
hsa-mir-93
|
MIR-17
|
AAAGUGC
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-95-P2_3p
|
-1.42
|
0.16
|
7.76e-18
|
40
|
142
|
hsa-mir-421
|
MIR-95
|
UCAACAG
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-17-P3a_5p
|
-1.41
|
0.19
|
1.99e-13
|
437
|
1513
|
hsa-mir-20a
|
MIR-17
|
AAAGUGC
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-188-P2_5p
|
-1.33
|
0.15
|
3.22e-17
|
279
|
914
|
hsa-mir-532
|
MIR-188
|
AUGCCUU
|
chrX
|
|
na
|
na
|
|
Hsa-Let-7-P2c2_5p
|
-1.26
|
0.14
|
4.74e-19
|
1738
|
5432
|
hsa-let-7i
|
LET-7
|
GAGGUAG
|
chr12
|
|
na
|
na
|
|
Hsa-Let-7-P2b3_5p
|
-1.22
|
0.15
|
7.97e-15
|
760
|
2149
|
hsa-mir-98
|
LET-7
|
GAGGUAG
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-210_3p
|
-1.16
|
0.23
|
5.10e-06
|
106
|
291
|
hsa-mir-210
|
MIR-210
|
UGUGCGU
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-425_5p
|
-1.15
|
0.15
|
2.77e-13
|
247
|
700
|
hsa-mir-425
|
MIR-425
|
AUGACAC
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-19-P1_3p
|
-1.12
|
0.19
|
1.87e-08
|
134
|
379
|
hsa-mir-19a
|
MIR-19
|
GUGCAAA
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-132-P1_3p
|
-1.06
|
0.17
|
6.29e-10
|
61
|
165
|
hsa-mir-132
|
MIR-132
|
AACAGUC
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-130-P4a_3p
|
-0.99
|
0.10
|
5.70e-21
|
70
|
177
|
hsa-mir-454
|
MIR-130
|
AGUGCAA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-652_3p
|
-0.91
|
0.13
|
2.45e-11
|
44
|
106
|
hsa-mir-652
|
MIR-652
|
AUGGCGC
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-769_5p
|
-0.81
|
0.13
|
9.49e-10
|
190
|
439
|
hsa-mir-769
|
MIR-769
|
GAGACCU
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-362-P2_3p/P4_3p
|
-0.69
|
0.17
|
7.46e-05
|
211
|
441
|
hsa-mir-500a
|
MIR-362
|
UGCACCU
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-181-P2a_5p/P2b_5p
|
-0.67
|
0.15
|
1.77e-05
|
611
|
1251
|
hsa-mir-181b-1
|
MIR-181
|
ACAUUCA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-17-P3c_3p
|
-0.64
|
0.14
|
5.82e-06
|
105
|
212
|
hsa-mir-106b
|
MIR-17
|
AAAGUGC
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-19-P2a_3p/P2b_3p
|
-0.60
|
0.18
|
1.90e-03
|
677
|
1297
|
hsa-mir-19b-1
|
MIR-19
|
GUGCAAA
|
chr13
|
|
na
|
na
|
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 54
pCRC vs nLu
column='tissue.type'
tissue_type_A <- 'normal.lung'
tissue_type_B <- 'tumor.colorect'
norm_adj_up = "None"
norm_adj_down = "None"
pCRC_adj_up = "None"
pCRC_adj_down = "None"
coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
lfc.Threshold, rpm.Threshold,
norm_adj_up,
norm_adj_down)
dict_sig_mirna[paste(coef, "up", sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
tissue_type_A, tissue_type_B,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_normal.lung_vs_tumor.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM normal.lung
|
RPM tumor.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-335_5p
|
1.10
|
0.19
|
1.98e-08
|
300
|
150
|
hsa-mir-335
|
MIR-335
|
CAAGAGC
|
chr7
|
Retinal Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-451_5p
|
3.02
|
0.43
|
1.09e-11
|
12496
|
1565
|
hsa-mir-451a
|
MIR-451
|
AACCGUU
|
chr17
|
Red Blood Cell
|
na
|
na
|
|
Hsa-Mir-144_5p
|
2.99
|
0.37
|
6.58e-15
|
451
|
57
|
hsa-mir-144
|
MIR-144
|
GAUAUCA
|
chr17
|
Red Blood Cell
|
na
|
na
|
|
Hsa-Mir-145_5p
|
1.49
|
0.36
|
9.83e-05
|
2445
|
840
|
hsa-mir-145
|
MIR-145
|
UCCAGUU
|
chr5
|
Mesenchymal
|
na
|
na
|
|
Hsa-Mir-24-P1_3p/P2_3p
|
0.73
|
0.22
|
2.23e-03
|
1084
|
626
|
hsa-mir-24-2
|
MIR-24
|
GGCUCAG
|
chr19
|
Macrophage
|
na
|
na
|
|
Hsa-Mir-150_5p
|
1.73
|
0.39
|
1.07e-05
|
973
|
280
|
hsa-mir-150
|
MIR-150
|
CUCCCAA
|
chr19
|
Lymphocyte
|
na
|
na
|
|
Hsa-Mir-15-P1a_5p
|
0.96
|
0.17
|
2.82e-08
|
854
|
453
|
hsa-mir-15a
|
MIR-15
|
AGCAGCA
|
chr13
|
CD14+ Monocyte
|
na
|
na
|
|
Hsa-Mir-486_5p
|
3.15
|
0.41
|
6.09e-14
|
22081
|
2013
|
hsa-mir-486-1
|
MIR-486
|
CCUGUAC
|
chr8
|
c(“Platelet”, “Red Blood Cell”)
|
na
|
na
|
|
Hsa-Mir-126_5p
|
3.27
|
0.23
|
4.58e-44
|
33620
|
3455
|
hsa-mir-126
|
MIR-126
|
AUUAUUA
|
chr9
|
c(“Endothelial Cell”, “Platelet”)
|
na
|
na
|
|
Hsa-Mir-146-P2_5p
|
1.39
|
0.37
|
3.72e-04
|
17435
|
6279
|
hsa-mir-146b
|
MIR-146
|
GAGAACU
|
chr10
|
c(“Dendritic Cell”, “Lymphocyte”)
|
na
|
na
|
|
Hsa-Mir-342_3p
|
2.20
|
0.28
|
2.02e-14
|
681
|
145
|
hsa-mir-342
|
MIR-342
|
CUCACAC
|
chr14
|
c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”)
|
na
|
na
|
|
Hsa-Mir-184_3p
|
4.90
|
0.90
|
1.36e-07
|
111
|
1
|
hsa-mir-184
|
MIR-184
|
GGACGGA
|
chr15
|
|
na
|
na
|
|
Hsa-Mir-34-P2a_5p
|
4.41
|
0.45
|
6.03e-22
|
116
|
5
|
hsa-mir-34b
|
MIR-34
|
GGCAGUG
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-34-P2b_5p
|
4.40
|
0.39
|
1.18e-28
|
748
|
33
|
hsa-mir-34c
|
MIR-34
|
GGCAGUG
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-30-P1a_5p
|
4.21
|
0.25
|
3.44e-60
|
60860
|
3165
|
hsa-mir-30a
|
MIR-30
|
GUAAACA
|
chr6
|
|
na
|
na
|
|
Hsa-Mir-218-P1_5p/P2_5p
|
3.31
|
0.30
|
3.30e-27
|
374
|
37
|
hsa-mir-218-1
|
MIR-218
|
UGUGCUU
|
chr4
|
|
na
|
na
|
|
Hsa-Mir-15-P2c_5p
|
2.58
|
0.26
|
1.28e-21
|
1552
|
257
|
hsa-mir-195
|
MIR-15
|
AGCAGCA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-338-P1_3p
|
2.57
|
0.35
|
1.28e-12
|
605
|
98
|
hsa-mir-338
|
MIR-338
|
CCAGCAU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-10-P2c_5p
|
2.37
|
0.37
|
4.44e-10
|
804
|
142
|
hsa-mir-99a
|
MIR-10
|
ACCCGUA
|
chr21
|
|
na
|
na
|
|
Hsa-Mir-10-P3b_5p
|
2.28
|
0.30
|
5.11e-13
|
8561
|
1649
|
hsa-mir-125a
|
MIR-10
|
CCCUGAG
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-130-P1a_3p
|
2.26
|
0.22
|
6.52e-23
|
1941
|
421
|
hsa-mir-130a
|
MIR-130
|
AGUGCAA
|
chr11
|
|
na
|
na
|
|
Hsa-Let-7-P1c_5p
|
2.02
|
0.31
|
4.62e-10
|
2606
|
606
|
hsa-let-7c
|
LET-7
|
GAGGUAG
|
chr21
|
|
na
|
na
|
|
Hsa-Mir-181-P1a_5p/P1b_5p
|
1.97
|
0.21
|
1.65e-19
|
80063
|
21233
|
hsa-mir-181a-1
|
MIR-181
|
ACAUUCA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-30-P1c_5p
|
1.87
|
0.18
|
4.93e-24
|
34088
|
9703
|
hsa-mir-30d
|
MIR-30
|
GUAAACA
|
chr8
|
|
na
|
na
|
|
Hsa-Mir-101-P1_3p/P2_3p
|
1.85
|
0.20
|
1.47e-19
|
16084
|
4638
|
hsa-mir-101-1
|
MIR-101
|
UACAGUA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-26-P1_5p/P2_5p
|
1.82
|
0.15
|
1.88e-32
|
123947
|
35584
|
hsa-mir-26b
|
MIR-26
|
UCAAGUA
|
chr2
|
|
na
|
na
|
|
Hsa-Mir-10-P3a_5p
|
1.72
|
0.35
|
2.27e-06
|
507
|
145
|
hsa-mir-125b-1
|
MIR-10
|
CCCUGAG
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-181-P2a_5p/P2b_5p
|
1.68
|
0.20
|
4.86e-16
|
3799
|
1251
|
hsa-mir-181b-1
|
MIR-181
|
ACAUUCA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-10-P3c_5p
|
1.60
|
0.34
|
3.99e-06
|
822
|
239
|
hsa-mir-125b-2
|
MIR-10
|
CCCUGAG
|
chr21
|
|
na
|
na
|
|
Hsa-Mir-140_3p
|
1.56
|
0.17
|
1.01e-18
|
2195
|
784
|
hsa-mir-140
|
MIR-140
|
CCACAGG
|
chr16
|
|
na
|
na
|
|
Hsa-Mir-15-P1c_5p
|
1.56
|
0.25
|
2.16e-09
|
405
|
143
|
hsa-mir-497
|
MIR-15
|
AGCAGCA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-30-P2a_5p/P2b_5p/P2c_5p
|
1.54
|
0.17
|
1.46e-19
|
7603
|
2793
|
hsa-mir-30c-2
|
MIR-30
|
GUAAACA
|
chr6
|
|
na
|
na
|
|
Hsa-Mir-92-P2b_3p
|
1.53
|
0.36
|
8.68e-05
|
137
|
47
|
hsa-mir-363
|
MIR-92
|
AUUGCAC
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-15-P2a_5p/P2b_5p
|
1.48
|
0.19
|
3.24e-14
|
12836
|
4522
|
hsa-mir-16-1
|
MIR-15
|
AGCAGCA
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-26-P3_5p
|
1.37
|
0.22
|
1.19e-09
|
9029
|
3467
|
hsa-mir-26a-1
|
MIR-26
|
UCAAGUA
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-10-P2a_5p
|
1.32
|
0.36
|
6.59e-04
|
4056
|
1724
|
hsa-mir-100
|
MIR-10
|
ACCCGUA
|
chr11
|
|
na
|
na
|
|
Hsa-Let-7-P2b1_5p
|
1.23
|
0.14
|
9.88e-19
|
6253
|
2784
|
hsa-let-7f-1
|
LET-7
|
GAGGUAG
|
chr9
|
|
na
|
na
|
|
Hsa-Mir-331_3p
|
1.11
|
0.29
|
2.07e-04
|
128
|
60
|
hsa-mir-331
|
MIR-331
|
CCCCUGG
|
chr12
|
|
na
|
na
|
|
Hsa-Mir-10-P2b_5p
|
1.06
|
0.42
|
2.28e-02
|
5968
|
2594
|
hsa-mir-99b
|
MIR-10
|
ACCCGUA
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-148-P2_3p
|
0.98
|
0.34
|
6.10e-03
|
270
|
137
|
hsa-mir-152
|
MIR-148
|
CAGUGCA
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-29-P1b_3p
|
0.96
|
0.23
|
6.29e-05
|
607
|
331
|
hsa-mir-29c
|
MIR-29
|
AGCACCA
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-652_3p
|
0.88
|
0.18
|
1.77e-06
|
187
|
106
|
hsa-mir-652
|
MIR-652
|
AUGGCGC
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-744_5p
|
0.88
|
0.24
|
5.22e-04
|
109
|
61
|
hsa-mir-744
|
MIR-744
|
GCGGGGC
|
chr17
|
|
na
|
na
|
|
Hsa-Let-7-P2b2_5p
|
0.84
|
0.17
|
3.85e-06
|
6575
|
3787
|
hsa-let-7b
|
LET-7
|
GAGGUAG
|
chr22
|
|
na
|
na
|
|
Hsa-Mir-27-P1_3p/P2_3p
|
0.80
|
0.16
|
4.13e-06
|
50854
|
29444
|
hsa-mir-27a
|
MIR-27
|
UCACAGU
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-374-P2_5p
|
0.74
|
0.22
|
1.39e-03
|
148
|
95
|
hsa-mir-374b
|
MIR-374
|
UAUAAUA
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-23-P1_3p/P2_3p
|
0.68
|
0.17
|
1.35e-04
|
5786
|
3766
|
hsa-mir-23a
|
MIR-23
|
UCACAUU
|
chr19
|
|
na
|
na
|
|
Hsa-Mir-28-P2_3p
|
0.65
|
0.19
|
1.33e-03
|
6007
|
3830
|
hsa-mir-151a
|
MIR-28
|
CGAGGAG
|
chr8
|
|
na
|
na
|
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 48
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_normal.lung_vs_tumor.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM normal.lung
|
RPM tumor.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-128-P1_3p/P2_3p
|
-0.86
|
0.18
|
6.72e-06
|
116
|
233
|
hsa-mir-128-1
|
MIR-128
|
CACAGUG
|
chr2
|
Neural
|
na
|
na
|
|
Hsa-Mir-192-P1_5p/P2_5p
|
-6.50
|
0.36
|
4.31e-69
|
142
|
13717
|
hsa-mir-192
|
MIR-192
|
UGACCUA
|
chr11
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-8-P2a_3p
|
-3.88
|
0.26
|
9.46e-48
|
618
|
10330
|
hsa-mir-200b
|
MIR-8
|
AAUACUG
|
chr1
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-8-P2b_3p
|
-1.75
|
0.26
|
7.14e-11
|
766
|
2727
|
hsa-mir-200c
|
MIR-8
|
AAUACUG
|
chr12
|
Epithelial Cell
|
na
|
na
|
|
Hsa-Mir-17-P1a_5p/P1b_5p
|
-2.17
|
0.21
|
9.51e-23
|
184
|
900
|
hsa-mir-17
|
MIR-17
|
AAAGUGC
|
chr13
|
CD14+ Monocyte
|
na
|
na
|
|
Hsa-Mir-7-P1_5p/P2_5p/P3_5p
|
-5.51
|
0.42
|
5.13e-38
|
3
|
199
|
hsa-mir-7-1
|
MIR-7
|
GGAAGAC
|
chr9
|
c(“Islet Cell”, “Neural”)
|
na
|
na
|
|
Hsa-Mir-375_3p
|
-1.22
|
0.43
|
1.11e-02
|
2346
|
5263
|
hsa-mir-375
|
MIR-375
|
UUGUUCG
|
chr2
|
c(“Epithelial Cell”, “Islet Cell”, “Neural”)
|
na
|
na
|
|
Hsa-Mir-196-P1_5p/P2_5p
|
-6.60
|
0.38
|
7.65e-66
|
3
|
319
|
hsa-mir-196a-1
|
MIR-196
|
AGGUAGU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-194-P1_5p/P2_5p
|
-6.59
|
0.31
|
7.31e-100
|
58
|
6167
|
hsa-mir-194-2
|
MIR-194
|
GUAACAG
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-196-P3_5p
|
-6.49
|
0.43
|
9.67e-50
|
7
|
828
|
hsa-mir-196b
|
MIR-196
|
AGGUAGU
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-192-P1_5p
|
-6.17
|
0.31
|
1.45e-85
|
1684
|
132000
|
hsa-mir-192
|
MIR-192
|
UGACCUA
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-577_5p
|
-5.61
|
0.37
|
3.74e-49
|
3
|
142
|
hsa-mir-577
|
MIR-577
|
UAGAUAA
|
chr4
|
|
na
|
na
|
|
Hsa-Mir-31_5p
|
-4.10
|
0.59
|
3.10e-12
|
24
|
627
|
hsa-mir-31
|
MIR-31
|
GGCAAGA
|
chr9
|
|
na
|
na
|
|
Hsa-Mir-8-P3a_3p
|
-2.93
|
0.27
|
4.00e-27
|
262
|
2303
|
hsa-mir-429
|
MIR-8
|
AAUACUG
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-17-P3a_5p
|
-2.82
|
0.26
|
3.30e-27
|
194
|
1513
|
hsa-mir-20a
|
MIR-17
|
AAAGUGC
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-135-P3_5p
|
-2.73
|
0.37
|
6.36e-13
|
20
|
155
|
hsa-mir-135b
|
MIR-135
|
AUGGCUU
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-127_3p
|
-2.70
|
0.36
|
7.71e-13
|
327
|
2263
|
hsa-mir-127
|
MIR-127
|
CGGAUCC
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-190-P1_5p
|
-2.56
|
0.29
|
1.90e-17
|
22
|
140
|
hsa-mir-190a
|
MIR-190
|
GAUAUGU
|
chr15
|
|
na
|
na
|
|
Hsa-Mir-224_5p
|
-2.55
|
0.30
|
2.30e-16
|
59
|
395
|
hsa-mir-224
|
MIR-224
|
AAGUCAC
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-154-P36_3p
|
-2.22
|
0.23
|
2.04e-21
|
39
|
195
|
hsa-mir-409
|
MIR-154
|
AAUGUUG
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-8-P1a_3p
|
-2.20
|
0.26
|
7.55e-16
|
353
|
1772
|
hsa-mir-200a
|
MIR-8
|
AACACUG
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-21_5p
|
-2.20
|
0.19
|
1.45e-28
|
21349
|
105735
|
hsa-mir-21
|
MIR-21
|
AGCUUAU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-15-P1d_5p
|
-2.15
|
0.34
|
9.48e-10
|
46
|
235
|
hsa-mir-424
|
MIR-15
|
AGCAGCA
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-17-P2a_5p
|
-2.09
|
0.31
|
5.05e-11
|
33
|
168
|
hsa-mir-18a
|
MIR-17
|
AAGGUGC
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-130-P1b_3p
|
-1.94
|
0.22
|
2.67e-18
|
28
|
116
|
hsa-mir-130b
|
MIR-130
|
AGUGCAA
|
chr22
|
|
na
|
na
|
|
Hsa-Mir-96-P3_5p
|
-1.94
|
0.37
|
3.95e-07
|
291
|
1063
|
hsa-mir-183
|
MIR-96
|
AUGGCAC
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-96-P2_5p
|
-1.91
|
0.27
|
1.11e-11
|
2685
|
10417
|
hsa-mir-182
|
MIR-96
|
UUGGCAA
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-154-P23_3p
|
-1.74
|
0.22
|
1.04e-13
|
41
|
148
|
hsa-mir-654
|
MIR-154
|
AUGUCUG
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-19-P1_3p
|
-1.65
|
0.26
|
1.09e-09
|
108
|
379
|
hsa-mir-19a
|
MIR-19
|
GUGCAAA
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-425_5p
|
-1.65
|
0.21
|
2.02e-14
|
213
|
700
|
hsa-mir-425
|
MIR-425
|
AUGACAC
|
chr3
|
|
na
|
na
|
|
Hsa-Mir-154-P12_3p
|
-1.63
|
0.24
|
1.28e-10
|
33
|
115
|
hsa-mir-410
|
MIR-154
|
AUAUAAC
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-10-P1b_5p
|
-1.55
|
0.41
|
4.05e-04
|
14125
|
39286
|
hsa-mir-10b
|
MIR-10
|
ACCCUGU
|
chr2
|
|
na
|
na
|
|
Hsa-Mir-378_3p
|
-1.45
|
0.22
|
9.02e-11
|
2189
|
6426
|
hsa-mir-378a
|
MIR-378
|
CUGGACU
|
chr5
|
|
na
|
na
|
|
Hsa-Mir-584_5p
|
-1.38
|
0.34
|
1.18e-04
|
39
|
110
|
hsa-mir-584
|
MIR-584
|
UAUGGUU
|
chr5
|
|
na
|
na
|
|
Hsa-Mir-92-P1a_3p/P1b_3p
|
-1.27
|
0.25
|
8.94e-07
|
15850
|
39320
|
hsa-mir-92a-1
|
MIR-92
|
AUUGCAC
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-17-P3c_5p
|
-1.21
|
0.18
|
2.57e-11
|
143
|
370
|
hsa-mir-106b
|
MIR-17
|
AAAGUGC
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-17-P1c_5p
|
-1.06
|
0.15
|
2.20e-11
|
1024
|
2287
|
hsa-mir-93
|
MIR-17
|
AAAGUGC
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-203_3p
|
-1.03
|
0.32
|
2.53e-03
|
1068
|
2307
|
hsa-mir-203a
|
MIR-203
|
UGAAAUG
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-210_3p
|
-1.01
|
0.31
|
4.02e-03
|
138
|
291
|
hsa-mir-210
|
MIR-210
|
UGUGCGU
|
chr11
|
|
na
|
na
|
|
Hsa-Mir-95-P2_3p
|
-0.99
|
0.22
|
1.35e-05
|
65
|
142
|
hsa-mir-421
|
MIR-95
|
UCAACAG
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-154-P13_5p
|
-0.93
|
0.24
|
3.43e-04
|
161
|
328
|
hsa-mir-411
|
MIR-154
|
AGUAGAC
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-193-P1b_3p
|
-0.92
|
0.26
|
8.53e-04
|
72
|
145
|
hsa-mir-193b
|
MIR-193
|
ACUGGCC
|
chr16
|
|
na
|
na
|
|
Hsa-Mir-19-P2a_3p/P2b_3p
|
-0.91
|
0.24
|
5.22e-04
|
645
|
1297
|
hsa-mir-19b-1
|
MIR-19
|
GUGCAAA
|
chr13
|
|
na
|
na
|
|
Hsa-Mir-148-P1_3p
|
-0.86
|
0.27
|
3.44e-03
|
18874
|
34704
|
hsa-mir-148a
|
MIR-148
|
CAGUGCA
|
chr7
|
|
na
|
na
|
|
Hsa-Mir-146-P1_5p
|
-0.85
|
0.39
|
4.76e-02
|
786
|
1453
|
hsa-mir-146a
|
MIR-146
|
GAGAACU
|
chr5
|
|
na
|
na
|
|
Hsa-Mir-136_3p
|
-0.81
|
0.24
|
1.54e-03
|
88
|
173
|
hsa-mir-136
|
MIR-136
|
AUCAUCG
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-10-P1a_5p
|
-0.79
|
0.34
|
4.36e-02
|
61989
|
97123
|
hsa-mir-10a
|
MIR-10
|
ACCCUGU
|
chr17
|
|
na
|
na
|
|
Hsa-Mir-154-P9_3p
|
-0.78
|
0.23
|
1.67e-03
|
135
|
253
|
hsa-mir-381
|
MIR-154
|
AUACAAG
|
chr14
|
|
na
|
na
|
|
Hsa-Mir-214_3p
|
-0.74
|
0.26
|
8.05e-03
|
77
|
137
|
hsa-mir-214
|
MIR-214
|
CAGCAGG
|
chr1
|
|
na
|
na
|
|
Hsa-Mir-1307_3p
|
-0.74
|
0.21
|
1.32e-03
|
80
|
139
|
hsa-mir-1307
|
MIR-1307
|
CGACCGG
|
chr10
|
|
na
|
na
|
|
Hsa-Let-7-P2b3_5p
|
-0.72
|
0.21
|
1.18e-03
|
1289
|
2149
|
hsa-mir-98
|
LET-7
|
GAGGUAG
|
chrX
|
|
na
|
na
|
|
Hsa-Mir-199-P1_3p/P2_3p/P3_3p
|
-0.64
|
0.22
|
5.97e-03
|
3382
|
5772
|
hsa-mir-199b
|
MIR-199
|
CAGUAGU
|
chr9
|
|
na
|
na
|
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 52
SubtractLFC <- function(x, y){
z = x
if ( is.na(x) | is.na(y) ){ return( z ) }
else if (sign(x) == sign(y)){ z = x - y }
if (sign(z) != sign(x)) { z = 0 }
return( z )
}
SubtractAdjP <- function(x , y, xP, yP){
z = xP
if ( is.na(xP) | is.na(yP) ){ return( z ) }
if ( sign(x) == sign(y) ){
z = (xP + ( 1 - yP )) }
if (z > 1) {z = 1}
return(z)
}
pCRC vs mLi
#pCRC versus liver metastasis, control also with pCRC versus normal liver
column='tissue.type'
tissue_type_A <- 'metastasis.liver'
tissue_type_B <- 'tumor.colorect'
norm_adj_up = dict_sig_mirna$tissue.type_normal.liver_vs_normal.colorect_up
norm_adj_down = dict_sig_mirna$tissue.type_normal.liver_vs_normal.colorect_down
pCRC_adj_up = dict_sig_mirna$tissue.type_normal.liver_vs_tumor.colorect_up
pCRC_adj_down = dict_sig_mirna$tissue.type_normal.liver_vs_tumor.colorect_down
palette <- 'jco'
coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
lfc.Threshold, rpm.Threshold,
norm_adj_up,
norm_adj_down,
pCRC_adj_up,
pCRC_adj_down)
dict_sig_mirna[paste(coef, "up", sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
tissue_type_A, tissue_type_B,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_metastasis.liver_vs_tumor.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM metastasis.liver
|
RPM tumor.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-210_3p
|
1.26
|
0.18
|
4.73e-11
|
685
|
291
|
hsa-mir-210
|
MIR-210
|
UGUGCGU
|
chr11
|
|
|
|
|
Hsa-Mir-592_5p
|
0.98
|
0.27
|
4.88e-03
|
137
|
75
|
hsa-mir-592
|
MIR-592
|
UGUGUCA
|
chr7
|
|
|
|
|
Hsa-Mir-10-P1a_5p
|
0.86
|
0.19
|
8.04e-05
|
164925
|
97123
|
hsa-mir-10a
|
MIR-10
|
ACCCUGU
|
chr17
|
|
|
|
|
Hsa-Mir-1307_5p
|
0.85
|
0.17
|
2.99e-06
|
887
|
492
|
hsa-mir-1307
|
MIR-1307
|
CGACCGG
|
chr10
|
|
|
|
|
Hsa-Mir-1247_5p
|
0.85
|
0.28
|
2.58e-02
|
127
|
69
|
hsa-mir-1247
|
MIR-1247
|
CCCGUCC
|
chr14
|
|
|
|
|
Hsa-Mir-191_5p
|
0.74
|
0.15
|
3.67e-06
|
23253
|
13630
|
hsa-mir-191
|
MIR-191
|
AACGGAA
|
chr3
|
|
|
|
|
Hsa-Mir-425_5p
|
0.73
|
0.12
|
2.90e-08
|
1117
|
700
|
hsa-mir-425
|
MIR-425
|
AUGACAC
|
chr3
|
|
|
|
|
Hsa-Mir-8-P1b_3p
|
0.64
|
0.19
|
4.07e-03
|
11957
|
7518
|
hsa-mir-141
|
MIR-8
|
AACACUG
|
chr12
|
|
|
|
|
Hsa-Mir-150_5p
|
1.08
|
0.22
|
2.99e-06
|
721
|
280
|
hsa-mir-150
|
MIR-150
|
CUCCCAA
|
chr19
|
Lymphocyte
|
|
yes
|
|
Hsa-Mir-342_3p
|
0.93
|
0.16
|
5.32e-08
|
320
|
145
|
hsa-mir-342
|
MIR-342
|
CUCACAC
|
chr14
|
c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”)
|
|
yes
|
|
Hsa-Mir-331_3p
|
0.75
|
0.16
|
3.16e-05
|
107
|
60
|
hsa-mir-331
|
MIR-331
|
CCCCUGG
|
chr12
|
|
|
yes
|
|
Hsa-Mir-15-P2a_5p/P2b_5p
|
0.68
|
0.11
|
1.34e-08
|
7588
|
4522
|
hsa-mir-16-1
|
MIR-15
|
AGCAGCA
|
chr13
|
|
|
yes
|
|
Hsa-Mir-204-P1_5p
|
1.04
|
0.32
|
1.21e-06
|
260
|
62
|
hsa-mir-204
|
MIR-204
|
UCCCUUU
|
chr9
|
Retinal Epithelial Cell
|
yes
|
yes
|
|
Hsa-Mir-335_5p
|
0.78
|
0.11
|
5.33e-11
|
261
|
150
|
hsa-mir-335
|
MIR-335
|
CAAGAGC
|
chr7
|
Retinal Epithelial Cell
|
yes
|
yes
|
|
Hsa-Mir-122_5p
|
5.14
|
0.43
|
2.78e-29
|
3668
|
9
|
hsa-mir-122
|
MIR-122
|
GGAGUGU
|
chr18
|
Hepatocyte
|
yes
|
yes
|
|
Hsa-Mir-10-P3c_5p
|
0.71
|
0.19
|
7.30e-04
|
480
|
239
|
hsa-mir-125b-2
|
MIR-10
|
CCCUGAG
|
chr21
|
|
yes
|
yes
|
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 16
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_metastasis.liver_vs_tumor.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM metastasis.liver
|
RPM tumor.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-486_5p
|
-0.66
|
0.23
|
3.47e-02
|
1489
|
2013
|
hsa-mir-486-1
|
MIR-486
|
CCUGUAC
|
chr8
|
c(“Platelet”, “Red Blood Cell”)
|
|
|
|
Hsa-Mir-31_5p
|
-2.34
|
0.31
|
4.05e-13
|
88
|
627
|
hsa-mir-31
|
MIR-31
|
GGCAAGA
|
chr9
|
|
|
yes
|
|
Hsa-Let-7-P2c2_5p
|
-0.90
|
0.11
|
1.69e-14
|
2840
|
5432
|
hsa-let-7i
|
LET-7
|
GAGGUAG
|
chr12
|
|
|
yes
|
|
Hsa-Mir-143_3p
|
-1.02
|
0.17
|
4.70e-08
|
72945
|
148431
|
hsa-mir-143
|
MIR-143
|
GAGAUGA
|
chr5
|
Mesenchymal
|
yes
|
yes
|
|
Hsa-Mir-133-P1_3p/P2_3p/P3_3p
|
-1.56
|
0.24
|
3.43e-10
|
38
|
116
|
hsa-mir-133a-2
|
MIR-133
|
UUGGUCC
|
chr20
|
c(“Skeletal Myocyte”, “Stem Cell”)
|
yes
|
yes
|
|
Hsa-Mir-10-P1b_5p
|
-1.62
|
0.23
|
1.76e-10
|
11785
|
39286
|
hsa-mir-10b
|
MIR-10
|
ACCCUGU
|
chr2
|
|
yes
|
yes
|
|
Hsa-Mir-92-P1c_3p
|
-0.68
|
0.17
|
2.83e-04
|
879
|
1398
|
hsa-mir-92b
|
MIR-92
|
AUUGCAC
|
chr1
|
|
yes
|
yes
|
|
Hsa-Mir-146-P1_5p
|
-0.63
|
0.22
|
1.91e-02
|
909
|
1453
|
hsa-mir-146a
|
MIR-146
|
GAGAACU
|
chr5
|
|
yes
|
yes
|
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 8
res_tibble <- res$res
res_tibble$miRNA <- rownames(res_tibble)
res_tibble <- as_tibble(res_tibble)
metslfc <- res_dict$tissue.type_metastasis.liver_vs_tumor.colorect$log2FoldChange
normlfc <- res_dict$tissue.type_normal.liver_vs_normal.colorect$log2FoldChange
res_tibble$LFC_adj_background <- mapply(SubtractLFC, metslfc, normlfc)
metsP <- res_dict$tissue.type_metastasis.liver_vs_tumor.colorect$padj
normP <- res_dict$tissue.type_normal.liver_vs_normal.colorect$padj
res_tibble$padj_subt_normal <- mapply( SubtractAdjP, metslfc, normlfc, metsP, normP )
res_tibble %>% select(miRNA, log2FoldChange, lfcSE, LFC_adj_background, padj_subt_normal, baseMean, stat, pvalue, padj) %>% write_csv(path = '/Users/eirikhoy/Dropbox/projects/comet_analysis/data/Deseq_result_clm_vs_pcrc.csv')
pCRC vs mLu
#pCRC versus lung metastasis, control also with pCRC versus normal liver
column='tissue.type'
tissue_type_A <- 'metastasis.lung'
tissue_type_B <- 'tumor.colorect'
norm_adj_up = dict_sig_mirna$tissue.type_normal.lung_vs_normal.colorect_up
norm_adj_down = dict_sig_mirna$tissue.type_normal.lung_vs_normal.colorect_down
pCRC_adj_up = dict_sig_mirna$tissue.type_normal.lung_vs_tumor.colorect_up
pCRC_adj_down = dict_sig_mirna$tissue.type_normal.lung_vs_tumor.colorect_down
coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
lfc.Threshold, rpm.Threshold,
norm_adj_up,
norm_adj_down,
pCRC_adj_up,
pCRC_adj_down)
dict_sig_mirna[paste(coef, "up", sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
tissue_type_A, tissue_type_B,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
signature_mirnas$up_mirna
Upregulated in tissue.type_metastasis.lung_vs_tumor.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM metastasis.lung
|
RPM tumor.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-155_5p
|
0.76
|
0.18
|
7.24e-05
|
733
|
387
|
hsa-mir-155
|
MIR-155
|
UAAUGCU
|
chr21
|
c(“Lymphocyte”, “Macrophage”)
|
|
|
|
Hsa-Mir-210_3p
|
1.15
|
0.19
|
1.55e-08
|
693
|
291
|
hsa-mir-210
|
MIR-210
|
UGUGCGU
|
chr11
|
|
|
|
|
Hsa-Mir-142_5p
|
0.84
|
0.18
|
2.01e-05
|
7117
|
3453
|
hsa-mir-142
|
MIR-142
|
AUAAAGU
|
chr17
|
|
|
|
|
Hsa-Mir-19-P2a_3p/P2b_3p
|
0.80
|
0.15
|
9.38e-07
|
2345
|
1297
|
hsa-mir-19b-1
|
MIR-19
|
GUGCAAA
|
chr13
|
|
|
|
|
Hsa-Mir-8-P1b_3p
|
0.75
|
0.21
|
1.12e-03
|
15111
|
7518
|
hsa-mir-141
|
MIR-8
|
AACACUG
|
chr12
|
|
|
|
|
Hsa-Mir-191_5p
|
0.74
|
0.16
|
9.81e-06
|
26989
|
13630
|
hsa-mir-191
|
MIR-191
|
AACGGAA
|
chr3
|
|
|
|
|
Hsa-Mir-374-P1_5p
|
0.72
|
0.12
|
9.07e-09
|
217
|
115
|
hsa-mir-374a
|
MIR-374
|
UAUAAUA
|
chrX
|
|
|
|
|
Hsa-Mir-19-P1_3p
|
0.65
|
0.16
|
3.42e-04
|
597
|
379
|
hsa-mir-19a
|
MIR-19
|
GUGCAAA
|
chr13
|
|
|
|
|
Hsa-Mir-145_5p
|
0.67
|
0.22
|
6.74e-03
|
1629
|
840
|
hsa-mir-145
|
MIR-145
|
UCCAGUU
|
chr5
|
Mesenchymal
|
|
yes
|
|
Hsa-Mir-150_5p
|
1.14
|
0.24
|
2.51e-06
|
738
|
280
|
hsa-mir-150
|
MIR-150
|
CUCCCAA
|
chr19
|
Lymphocyte
|
|
yes
|
|
Hsa-Mir-10-P3c_5p
|
0.85
|
0.21
|
9.78e-05
|
574
|
239
|
hsa-mir-125b-2
|
MIR-10
|
CCCUGAG
|
chr21
|
|
|
yes
|
|
Hsa-Mir-29-P1b_3p
|
0.71
|
0.14
|
3.39e-06
|
597
|
331
|
hsa-mir-29c
|
MIR-29
|
AGCACCA
|
chr1
|
|
|
yes
|
|
Hsa-Mir-15-P2a_5p/P2b_5p
|
0.71
|
0.12
|
1.51e-08
|
8258
|
4522
|
hsa-mir-16-1
|
MIR-15
|
AGCAGCA
|
chr13
|
|
|
yes
|
|
Hsa-Mir-26-P3_5p
|
0.65
|
0.14
|
6.79e-06
|
6205
|
3467
|
hsa-mir-26a-1
|
MIR-26
|
UCAAGUA
|
chr3
|
|
|
yes
|
|
Hsa-Mir-331_3p
|
0.62
|
0.18
|
1.05e-03
|
105
|
60
|
hsa-mir-331
|
MIR-331
|
CCCCUGG
|
chr12
|
|
|
yes
|
|
Hsa-Mir-335_5p
|
0.88
|
0.12
|
1.45e-12
|
295
|
150
|
hsa-mir-335
|
MIR-335
|
CAAGAGC
|
chr7
|
Retinal Epithelial Cell
|
yes
|
yes
|
|
Hsa-Mir-451_5p
|
0.87
|
0.26
|
1.88e-03
|
3152
|
1565
|
hsa-mir-451a
|
MIR-451
|
AACCGUU
|
chr17
|
Red Blood Cell
|
yes
|
yes
|
|
Hsa-Mir-24-P1_3p/P2_3p
|
0.74
|
0.14
|
7.89e-07
|
1289
|
626
|
hsa-mir-24-2
|
MIR-24
|
GGCUCAG
|
chr19
|
Macrophage
|
yes
|
yes
|
|
Hsa-Mir-126_5p
|
1.67
|
0.15
|
6.56e-29
|
13328
|
3455
|
hsa-mir-126
|
MIR-126
|
AUUAUUA
|
chr9
|
c(“Endothelial Cell”, “Platelet”)
|
yes
|
yes
|
|
Hsa-Mir-146-P2_5p
|
0.94
|
0.23
|
1.47e-04
|
15403
|
6279
|
hsa-mir-146b
|
MIR-146
|
GAGAACU
|
chr10
|
c(“Dendritic Cell”, “Lymphocyte”)
|
yes
|
yes
|
|
Hsa-Mir-342_3p
|
2.25
|
0.18
|
6.24e-36
|
872
|
145
|
hsa-mir-342
|
MIR-342
|
CUCACAC
|
chr14
|
c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”)
|
yes
|
yes
|
|
Hsa-Mir-34-P2b_5p
|
4.24
|
0.24
|
2.76e-67
|
989
|
33
|
hsa-mir-34c
|
MIR-34
|
GGCAGUG
|
chr11
|
|
yes
|
yes
|
|
Hsa-Mir-34-P2a_5p
|
3.97
|
0.27
|
8.59e-45
|
128
|
5
|
hsa-mir-34b
|
MIR-34
|
GGCAGUG
|
chr11
|
|
yes
|
yes
|
|
Hsa-Mir-30-P1a_5p
|
1.74
|
0.16
|
1.56e-26
|
13224
|
3165
|
hsa-mir-30a
|
MIR-30
|
GUAAACA
|
chr6
|
|
yes
|
yes
|
|
Hsa-Mir-15-P2c_5p
|
1.64
|
0.17
|
6.99e-22
|
975
|
257
|
hsa-mir-195
|
MIR-15
|
AGCAGCA
|
chr17
|
|
yes
|
yes
|
|
Hsa-Mir-218-P1_5p/P2_5p
|
1.57
|
0.19
|
3.94e-16
|
135
|
37
|
hsa-mir-218-1
|
MIR-218
|
UGUGCUU
|
chr4
|
|
yes
|
yes
|
|
Hsa-Mir-374-P2_5p
|
1.14
|
0.14
|
1.78e-15
|
226
|
95
|
hsa-mir-374b
|
MIR-374
|
UAUAAUA
|
chrX
|
|
yes
|
yes
|
|
Hsa-Mir-338-P1_3p
|
1.10
|
0.22
|
1.51e-06
|
236
|
98
|
hsa-mir-338
|
MIR-338
|
CCAGCAU
|
chr17
|
|
yes
|
yes
|
|
Hsa-Mir-26-P1_5p/P2_5p
|
1.07
|
0.10
|
7.47e-28
|
84008
|
35584
|
hsa-mir-26b
|
MIR-26
|
UCAAGUA
|
chr2
|
|
yes
|
yes
|
|
Hsa-Mir-10-P2c_5p
|
0.63
|
0.23
|
8.68e-03
|
289
|
142
|
hsa-mir-99a
|
MIR-10
|
ACCCGUA
|
chr21
|
|
yes
|
yes
|
|
Hsa-Mir-130-P1a_3p
|
0.62
|
0.14
|
4.12e-05
|
729
|
421
|
hsa-mir-130a
|
MIR-130
|
AGUGCAA
|
chr11
|
|
yes
|
yes
|
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 31
# Print list downregulated miRNA
signature_mirnas$down_mirna
Downregulated in tissue.type_metastasis.lung_vs_tumor.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM metastasis.lung
|
RPM tumor.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-423_5p
|
-1.17
|
0.17
|
1.35e-10
|
202
|
433
|
hsa-mir-423
|
MIR-423
|
GAGGGGC
|
chr17
|
|
|
|
|
Hsa-Let-7-P1b_5p
|
-0.86
|
0.16
|
2.55e-07
|
650
|
1057
|
hsa-let-7e
|
LET-7
|
GAGGUAG
|
chr19
|
|
|
|
|
Hsa-Mir-197_3p
|
-0.74
|
0.13
|
1.76e-07
|
113
|
181
|
hsa-mir-197
|
MIR-197
|
UCACCAC
|
chr1
|
|
|
|
|
Hsa-Mir-92-P1c_3p
|
-0.72
|
0.18
|
2.22e-04
|
981
|
1398
|
hsa-mir-92b
|
MIR-92
|
AUUGCAC
|
chr1
|
|
|
|
|
Hsa-Mir-362-P2_3p/P4_3p
|
-0.65
|
0.14
|
2.36e-05
|
302
|
441
|
hsa-mir-500a
|
MIR-362
|
UGCACCU
|
chrX
|
|
|
|
|
Hsa-Mir-221-P2_3p
|
-0.60
|
0.14
|
8.50e-05
|
1101
|
1633
|
hsa-mir-222
|
MIR-221
|
GCUACAU
|
chrX
|
|
|
|
|
Hsa-Mir-7-P1_5p/P2_5p/P3_5p
|
-0.80
|
0.25
|
1.88e-03
|
106
|
199
|
hsa-mir-7-1
|
MIR-7
|
GGAAGAC
|
chr9
|
c(“Islet Cell”, “Neural”)
|
|
yes
|
|
Hsa-Mir-154-P36_3p
|
-1.40
|
0.14
|
3.20e-21
|
80
|
195
|
hsa-mir-409
|
MIR-154
|
AAUGUUG
|
chr14
|
|
|
yes
|
|
Hsa-Mir-154-P12_3p
|
-1.38
|
0.15
|
7.64e-18
|
46
|
115
|
hsa-mir-410
|
MIR-154
|
AUAUAAC
|
chr14
|
|
|
yes
|
|
Hsa-Mir-31_5p
|
-1.01
|
0.34
|
1.99e-03
|
242
|
627
|
hsa-mir-31
|
MIR-31
|
GGCAAGA
|
chr9
|
|
|
yes
|
|
Hsa-Mir-96-P2_5p
|
-0.78
|
0.17
|
1.58e-05
|
6399
|
10417
|
hsa-mir-182
|
MIR-96
|
UUGGCAA
|
chr7
|
|
|
yes
|
|
Hsa-Mir-199-P1_3p/P2_3p/P3_3p
|
-0.65
|
0.14
|
1.06e-05
|
3904
|
5772
|
hsa-mir-199b
|
MIR-199
|
CAGUAGU
|
chr9
|
|
|
yes
|
|
Hsa-Mir-15-P1d_5p
|
-0.63
|
0.21
|
5.51e-03
|
158
|
235
|
hsa-mir-424
|
MIR-15
|
AGCAGCA
|
chrX
|
|
|
yes
|
|
Hsa-Mir-92-P1a_3p/P1b_3p
|
-0.63
|
0.15
|
1.76e-04
|
26467
|
39320
|
hsa-mir-92a-1
|
MIR-92
|
AUUGCAC
|
chr13
|
|
|
yes
|
|
Hsa-Mir-143_3p
|
-1.34
|
0.19
|
1.00e-11
|
67942
|
148431
|
hsa-mir-143
|
MIR-143
|
GAGAUGA
|
chr5
|
Mesenchymal
|
yes
|
|
|
Hsa-Mir-133-P1_3p/P2_3p/P3_3p
|
-1.73
|
0.26
|
4.97e-11
|
37
|
116
|
hsa-mir-133a-2
|
MIR-133
|
UUGGUCC
|
chr20
|
c(“Skeletal Myocyte”, “Stem Cell”)
|
yes
|
|
|
Hsa-Mir-362-P3_3p
|
-0.89
|
0.25
|
1.47e-03
|
61
|
108
|
hsa-mir-501
|
MIR-362
|
AUGCACC
|
chrX
|
|
yes
|
|
|
Hsa-Mir-8-P2a_3p
|
-0.60
|
0.16
|
6.49e-04
|
6834
|
10330
|
hsa-mir-200b
|
MIR-8
|
AAUACUG
|
chr1
|
Epithelial Cell
|
yes
|
yes
|
|
Hsa-Mir-127_3p
|
-1.99
|
0.22
|
3.22e-17
|
617
|
2263
|
hsa-mir-127
|
MIR-127
|
CGGAUCC
|
chr14
|
|
yes
|
yes
|
|
Hsa-Mir-154-P13_5p
|
-1.34
|
0.15
|
3.38e-17
|
142
|
328
|
hsa-mir-411
|
MIR-154
|
AGUAGAC
|
chr14
|
|
yes
|
yes
|
|
Hsa-Mir-154-P9_3p
|
-1.34
|
0.14
|
1.80e-18
|
107
|
253
|
hsa-mir-381
|
MIR-154
|
AUACAAG
|
chr14
|
|
yes
|
yes
|
|
Hsa-Mir-190-P1_5p
|
-1.26
|
0.18
|
2.77e-11
|
58
|
140
|
hsa-mir-190a
|
MIR-190
|
GAUAUGU
|
chr15
|
|
yes
|
yes
|
|
Hsa-Mir-154-P23_3p
|
-1.22
|
0.14
|
1.46e-16
|
69
|
148
|
hsa-mir-654
|
MIR-154
|
AUGUCUG
|
chr14
|
|
yes
|
yes
|
|
Hsa-Mir-136_3p
|
-1.09
|
0.15
|
5.02e-12
|
86
|
173
|
hsa-mir-136
|
MIR-136
|
AUCAUCG
|
chr14
|
|
yes
|
yes
|
|
Hsa-Mir-378_3p
|
-0.69
|
0.14
|
2.25e-06
|
3998
|
6426
|
hsa-mir-378a
|
MIR-378
|
CUGGACU
|
chr5
|
|
yes
|
yes
|
|
Hsa-Mir-10-P1b_5p
|
-0.66
|
0.25
|
1.93e-02
|
30285
|
39286
|
hsa-mir-10b
|
MIR-10
|
ACCCUGU
|
chr2
|
|
yes
|
yes
|
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 26
res_tibble <- res$res
res_tibble$miRNA <- rownames(res_tibble)
res_tibble <- as_tibble(res_tibble)
metslfc <- res_dict$tissue.type_metastasis.lung_vs_tumor.colorect$log2FoldChange
normlfc <- res_dict$tissue.type_normal.lung_vs_normal.colorect$log2FoldChange
res_tibble$LFC_adj_background <- mapply(SubtractLFC, metslfc, normlfc)
metsP <- res_dict$tissue.type_metastasis.lung_vs_tumor.colorect$padj
normP <- res_dict$tissue.type_normal.lung_vs_normal.colorect$padj
res_tibble$padj_subt_normal <- mapply(SubtractAdjP, metslfc, normlfc,metsP, normP)
res_tibble %>% select(miRNA, log2FoldChange, lfcSE, LFC_adj_background, padj_subt_normal, baseMean, stat, pvalue, padj) %>% write_csv(path =
'/Users/eirikhoy/Dropbox/projects/comet_analysis/data/Deseq_result_mlu_vs_pcrc.csv')
pCRC vs PM
#pCRC versus PC metastasis, union of mLi and mLu normal control
column='tissue.type'
tissue_type_A <- 'metastasis.pc'
tissue_type_B <- 'tumor.colorect'
norm_adj_up = union(dict_sig_mirna$tissue.type_normal.liver_vs_normal.colorect_up, dict_sig_mirna$tissue.type_normal.lung_vs_normal.colorect_up)
norm_adj_down = union(dict_sig_mirna$tissue.type_normal.liver_vs_normal.colorect_down, dict_sig_mirna$tissue.type_normal.lung_vs_normal.colorect_down)
pCRC_adj_up = union(dict_sig_mirna$tissue.type_normal.liver_vs_tumor.colorect_up, dict_sig_mirna$tissue.type_normal.lung_vs_tumor.colorect_up)
pCRC_adj_down = union(dict_sig_mirna$tissue.type_normal.liver_vs_tumor.colorect_down, dict_sig_mirna$tissue.type_normal.lung_vs_tumor.colorect_down)
coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
lfc.Threshold, rpm.Threshold,
norm_adj_up,
norm_adj_down,
pCRC_adj_up,
pCRC_adj_down)
dict_sig_mirna[paste(coef, "up", sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

ExpressionPlot(res$res, res$rpm, coef, res$sig,
tissue_type_A, tissue_type_B,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)

signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)
# Print list upregulated miRNA
print("as no normal adjacent PC tissue was available, control was union of lung and liver normal adjacent")
## [1] "as no normal adjacent PC tissue was available, control was union of lung and liver normal adjacent"
signature_mirnas$up_mirna
Upregulated in tissue.type_metastasis.pc_vs_tumor.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM metastasis.pc
|
RPM tumor.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-155_5p
|
0.70
|
0.17
|
1.59e-04
|
654
|
387
|
hsa-mir-155
|
MIR-155
|
UAAUGCU
|
chr21
|
c(“Lymphocyte”, “Macrophage”)
|
|
|
|
Hsa-Mir-506-P4a1_3p/P4a2_3p/P4b_3p
|
2.72
|
0.29
|
5.75e-11
|
191
|
14
|
hsa-mir-509-1
|
MIR-506
|
GAUUGGU
|
chrX
|
|
|
|
|
Hsa-Mir-506-P3_3p
|
2.69
|
0.29
|
6.35e-09
|
112
|
6
|
hsa-mir-508
|
MIR-506
|
GAUUGUA
|
chrX
|
|
|
|
|
Hsa-Mir-154-P9_3p
|
0.85
|
0.14
|
1.43e-08
|
455
|
253
|
hsa-mir-381
|
MIR-154
|
AUACAAG
|
chr14
|
|
|
|
|
Hsa-Mir-154-P36_3p
|
0.74
|
0.13
|
7.13e-07
|
330
|
195
|
hsa-mir-409
|
MIR-154
|
AAUGUUG
|
chr14
|
|
|
|
|
Hsa-Mir-210_3p
|
0.71
|
0.18
|
3.37e-04
|
491
|
291
|
hsa-mir-210
|
MIR-210
|
UGUGCGU
|
chr11
|
|
|
|
|
Hsa-Mir-1307_5p
|
0.62
|
0.17
|
8.29e-04
|
797
|
492
|
hsa-mir-1307
|
MIR-1307
|
CGACCGG
|
chr10
|
|
|
|
|
Hsa-Mir-127_3p
|
0.61
|
0.20
|
1.42e-02
|
2888
|
2263
|
hsa-mir-127
|
MIR-127
|
CGGAUCC
|
chr14
|
|
|
|
|
Hsa-Mir-191_5p
|
0.61
|
0.15
|
2.41e-04
|
20429
|
13630
|
hsa-mir-191
|
MIR-191
|
AACGGAA
|
chr3
|
|
|
|
|
Hsa-Mir-150_5p
|
0.70
|
0.21
|
2.45e-03
|
528
|
280
|
hsa-mir-150
|
MIR-150
|
CUCCCAA
|
chr19
|
Lymphocyte
|
|
yes
|
|
Hsa-Mir-154-P13_5p
|
0.94
|
0.14
|
2.08e-09
|
628
|
328
|
hsa-mir-411
|
MIR-154
|
AGUAGAC
|
chr14
|
|
|
yes
|
|
Hsa-Mir-15-P2a_5p/P2b_5p
|
0.80
|
0.11
|
7.44e-11
|
8204
|
4522
|
hsa-mir-16-1
|
MIR-15
|
AGCAGCA
|
chr13
|
|
|
yes
|
|
Hsa-Mir-136_3p
|
0.69
|
0.14
|
1.18e-05
|
274
|
173
|
hsa-mir-136
|
MIR-136
|
AUCAUCG
|
chr14
|
|
|
yes
|
|
Hsa-Mir-335_5p
|
0.59
|
0.11
|
2.33e-06
|
232
|
150
|
hsa-mir-335
|
MIR-335
|
CAAGAGC
|
chr7
|
Retinal Epithelial Cell
|
yes
|
yes
|
|
Hsa-Mir-122_5p
|
1.45
|
0.29
|
9.08e-08
|
244
|
9
|
hsa-mir-122
|
MIR-122
|
GGAGUGU
|
chr18
|
Hepatocyte
|
yes
|
yes
|
|
Hsa-Mir-486_5p
|
0.76
|
0.22
|
2.47e-03
|
4442
|
2013
|
hsa-mir-486-1
|
MIR-486
|
CCUGUAC
|
chr8
|
c(“Platelet”, “Red Blood Cell”)
|
yes
|
yes
|
|
Hsa-Mir-126_5p
|
0.63
|
0.14
|
2.84e-05
|
5738
|
3455
|
hsa-mir-126
|
MIR-126
|
AUUAUUA
|
chr9
|
c(“Endothelial Cell”, “Platelet”)
|
yes
|
yes
|
|
Hsa-Mir-342_3p
|
0.76
|
0.16
|
1.39e-05
|
275
|
145
|
hsa-mir-342
|
MIR-342
|
CUCACAC
|
chr14
|
c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”)
|
yes
|
yes
|
|
Hsa-Mir-10-P2c_5p
|
1.10
|
0.20
|
1.07e-06
|
349
|
142
|
hsa-mir-99a
|
MIR-10
|
ACCCGUA
|
chr21
|
|
yes
|
yes
|
|
Hsa-Let-7-P1c_5p
|
0.95
|
0.18
|
1.40e-06
|
1282
|
606
|
hsa-let-7c
|
LET-7
|
GAGGUAG
|
chr21
|
|
yes
|
yes
|
|
Hsa-Mir-10-P3c_5p
|
0.80
|
0.19
|
1.02e-04
|
497
|
239
|
hsa-mir-125b-2
|
MIR-10
|
CCCUGAG
|
chr21
|
|
yes
|
yes
|
|
Hsa-Mir-154-P23_3p
|
0.80
|
0.13
|
5.21e-08
|
256
|
148
|
hsa-mir-654
|
MIR-154
|
AUGUCUG
|
chr14
|
|
yes
|
yes
|
|
Hsa-Mir-130-P1a_3p
|
0.70
|
0.13
|
1.40e-06
|
728
|
421
|
hsa-mir-130a
|
MIR-130
|
AGUGCAA
|
chr11
|
|
yes
|
yes
|
|
Hsa-Mir-15-P2c_5p
|
0.60
|
0.15
|
3.85e-04
|
413
|
257
|
hsa-mir-195
|
MIR-15
|
AGCAGCA
|
chr17
|
|
yes
|
yes
|
|
Hsa-Mir-26-P1_5p/P2_5p
|
0.59
|
0.09
|
3.22e-09
|
52635
|
35584
|
hsa-mir-26b
|
MIR-26
|
UCAAGUA
|
chr2
|
|
yes
|
yes
|
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 25
# Print list downregulated miRNA
print("as no normal adjacent PC tissue was available, control was union of lung and liver normal adjacent")
## [1] "as no normal adjacent PC tissue was available, control was union of lung and liver normal adjacent"
signature_mirnas$down_mirna
Downregulated in tissue.type_metastasis.pc_vs_tumor.colorect
|
miRNA
|
LFC
|
lfcSE
|
FDR
|
RPM metastasis.pc
|
RPM tumor.colorect
|
miRBase_ID
|
Family
|
Seed
|
Chr
|
Cell-Type Specific
|
Norm Background
|
pCRC Background
|
|
Hsa-Mir-223_3p
|
-0.94
|
0.21
|
4.62e-05
|
280
|
619
|
hsa-mir-223
|
MIR-223
|
GUCAGUU
|
chrX
|
c(“Dendritic Cell”, “Macrophage”)
|
|
|
|
Hsa-Mir-143_3p
|
-0.86
|
0.17
|
6.37e-06
|
77753
|
148431
|
hsa-mir-143
|
MIR-143
|
GAGAUGA
|
chr5
|
Mesenchymal
|
yes
|
yes
|
|
Hsa-Mir-133-P1_3p/P2_3p/P3_3p
|
-1.82
|
0.23
|
3.20e-14
|
25
|
116
|
hsa-mir-133a-2
|
MIR-133
|
UUGGUCC
|
chr20
|
c(“Skeletal Myocyte”, “Stem Cell”)
|
yes
|
yes
|
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 3
res_tibble <- res$res
res_tibble$miRNA <- rownames(res_tibble)
res_tibble <- as_tibble(res_tibble)
metslfc <- res_dict$tissue.type_metastasis.pc_vs_tumor.colorect$log2FoldChange
normlfc <- ( (res_dict$tissue.type_normal.lung_vs_normal.colorect$log2FoldChange + res_dict$tissue.type_normal.liver_vs_normal.colorect$log2FoldChange) / 2)
res_tibble$LFC_adj_background <- mapply(SubtractLFC, metslfc, normlfc)
metsP <- res_dict$tissue.type_metastasis.pc_vs_tumor.colorect$padj
normP <- ( (res_dict$tissue.type_normal.lung_vs_normal.colorect$padj - res_dict$tissue.type_normal.liver_vs_normal.colorect$padj) / 2 )
res_tibble$padj_subt_normal <- mapply(SubtractAdjP, metslfc, normlfc,metsP, normP)
res_tibble %>% select(miRNA, log2FoldChange, lfcSE, LFC_adj_background, padj_subt_normal, baseMean, stat, pvalue, padj) %>% write_csv(path = '/Users/eirikhoy/Dropbox/projects/comet_analysis/data/Deseq_result_pc_vs_pcrc.csv')
All mCRC vs pCRC
DeseqObject <- function(DESIGN, countdata, coldata, consensus="None", sample_type="None", Ref) {
"
Function to create DESeq2 object
"
dds <- DESeqDataSetFromMatrix(countData = countdata,
colData = coldata,
design = as.formula(paste("~", DESIGN)))
# Kick out non-consensus samples
if (!(consensus == "None")) {
dds <- dds[, dds$paper %in% consensus]
}
# Kick out samples that are not bulk tissue
if (!(sample_type == "None")) {
dds <- dds[, dds$sample_type == sample_type]
}
dds$type <- relevel(dds$type, ref=ref)
dds$type <- droplevels(dds$type)
dds <- DESeq(dds,
parallel=TRUE,
BPPARAM=MulticoreParam(3)
)
return(dds)
}
SigList <- function(res, dds, tissue_type_A, tissue_type_B, coef,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down){
"
Function to create annotated lists of signature miRNA
Return will print upregulated or downregulated miRNA,
by printing <signature_list>$up_mirna
or <signature_list>$down_mirna
"
group_A_rpm <- rowMeans(res$rpm[res$sig, dds$type == tissue_type_A])
group_A_rpm_std <- rowSds(res$rpm[res$sig, dds$type == tissue_type_A])
group_B_rpm <- rowMeans(res$rpm[res$sig, dds$type == tissue_type_B])
group_B_rpm_std <- rowSds(res$rpm[res$sig, dds$type == tissue_type_B])
lfc.deseq2 <- res$res[res$sig, ]$log2FoldChange
lfcSE.deseq2<- res$res[res$sig, ]$lfcSE
neg.log.10.adj.p <- -log10(res$res[res$sig, ]$padj)
signature_mirna <- res$sig
sig_list <- dplyr::tibble(signature_mirna, lfc.deseq2, lfcSE.deseq2,
group_A_rpm, #group_A_rpm_std,
group_B_rpm, #group_B_rpm_std,
neg.log.10.adj.p)
# create list of upregulated mirna
up_mirna <- sig_list %>%
filter(lfc.deseq2 > lfc.Threshold) %>%
# Annotate which miRNA are cell markers
mutate(
cell_marker = ifelse(signature_mirna %in% names(cell_spec_dict_inv), cell_spec_dict_inv[signature_mirna], '')) %>%
mutate(
cell_marker = cell_spec(cell_marker, color = ifelse(cell_marker != '', 'white', 'black'),
background = ifelse(cell_marker != '', 'blue', 'white'),
bold = ifelse(cell_marker != '', F, F))) %>%
# Annotate which miRNA are present in blood cells
mutate(
blood_cell = ifelse(signature_mirna %in% blood.cell.mirna, 'yes', '')) %>%
mutate(
blood_cell = cell_spec(blood_cell, color = ifelse(blood_cell == 'yes', 'white', 'black'),
background = ifelse(blood_cell == 'yes', 'red', 'white'),
bold = ifelse(blood_cell == 'yes', T, F)))
# Annotate which miRNA are in normal_adjacent
if (norm_adj_up != "None") {
up_mirna <- up_mirna %>%
mutate(
norm_adj = ifelse(signature_mirna %in% norm_adj_up, 'yes', '')) %>%
mutate(
norm_adj = cell_spec(norm_adj, color = ifelse(norm_adj == 'yes', 'white', 'black'),
background = ifelse(norm_adj == 'yes', 'black', 'white'),
bold = ifelse(norm_adj == 'yes', T, F))
)
}
else up_mirna$norm_adj <- "na"
# Annotate which miRNA are in pCRC_adjacent
if (pCRC_adj_up != "None") {
up_mirna <- up_mirna %>%
mutate(
pCRC_adj = ifelse(signature_mirna %in% pCRC_adj_up, 'yes', '')) %>%
mutate(
pCRC_adj = cell_spec(pCRC_adj, color = ifelse(pCRC_adj == 'yes', 'white', 'black'),
background = ifelse(pCRC_adj == 'yes', 'black', 'white'),
bold = ifelse(pCRC_adj == 'yes', T, F))
)
}
else up_mirna$pCRC_adj <- "na"
# number of upregulated miRNA
number_upregulated <- dim(up_mirna)[1]
# Create kable list with annotations
up_mirna <- up_mirna %>%
arrange(-lfc.deseq2) %>%
arrange(desc(cell_marker)) %>%
arrange(pCRC_adj) %>%
arrange(norm_adj) %>%
kable(col.names = c("miRNA", "LFC", "lfcSE",
paste('RPM', tissue_type_A), #paste('std', tissue_type_A),
paste('RPM', tissue_type_B), #paste('std', tissue_type_B),
"-log10(adj p-value)", "cell_marker", "blood_cell", 'norm_adj', 'pCRC_adj'),
escape = F, booktabs = F, caption = paste("Upregulated in ", coef),
digits = c(0, 2, 2, 0, 0, 3, 2, 3, 0, 0, 0, 0)) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T,
fixed_thead = list(enabled = T)) %>%
column_spec(2, bold = T)
# create list of downregulated miRNA
down_mirna <- sig_list %>%
filter(lfc.deseq2 < -lfc.Threshold) %>%
# Annotate which miRNA are cell markers
mutate(
cell_marker = ifelse(signature_mirna %in% names(cell_spec_dict_inv), cell_spec_dict_inv[signature_mirna], '')) %>%
mutate(
cell_marker = cell_spec(cell_marker, color = ifelse(cell_marker != '', 'white', 'black'),
background = ifelse(cell_marker != '', 'blue', 'white'),
bold = ifelse(cell_marker != '', F, F))) %>%
# Annotate which miRNA are present in blood cells
mutate(
blood_cell = ifelse(signature_mirna %in% blood.cell.mirna, 'yes', '')) %>%
mutate(
blood_cell = cell_spec(blood_cell, color = ifelse(blood_cell == 'yes', 'white', 'black'),
background = ifelse(blood_cell == 'yes', 'red', 'white'),
bold = ifelse(blood_cell == 'yes', T, F)))
# Annotate which miRNA are in normal_adjacent
if (norm_adj_down != "None") {
down_mirna <- down_mirna %>%
mutate(
norm_adj = ifelse(signature_mirna %in% norm_adj_down, 'yes', '')) %>%
mutate(
norm_adj = cell_spec(norm_adj, color = ifelse(norm_adj == 'yes', 'white', 'black'),
background = ifelse(norm_adj == 'yes', 'black', 'white'),
bold = ifelse(norm_adj == 'yes', T, F))
)
}
else down_mirna$norm_adj <- "na"
# Annotate which miRNA are in pCRC_adjacent
if (pCRC_adj_down != "None") {
down_mirna <- down_mirna %>%
mutate(
pCRC_adj = ifelse(signature_mirna %in% pCRC_adj_down, 'yes', '')) %>%
mutate(
pCRC_adj = cell_spec(pCRC_adj, color = ifelse(pCRC_adj == 'yes', 'white', 'black'),
background = ifelse(pCRC_adj == 'yes', 'black', 'white'),
bold = ifelse(pCRC_adj == 'yes', T, F))
)
}
else down_mirna$pCRC_adj <- "na"
# number of upregulated miRNA
number_downregulated <- dim(down_mirna)[1]
# Create kable list with annotations
down_mirna <- down_mirna %>%
arrange(lfc.deseq2) %>%
arrange(desc(cell_marker)) %>%
arrange(pCRC_adj) %>%
arrange(norm_adj) %>%
kable(col.names = c("miRNA", "LFC", "lfcSE",
paste('RPM', tissue_type_A), #paste('std', tissue_type_A),
paste('RPM', tissue_type_B), #paste('std', tissue_type_B),
"-log10(adj p-value)", "cell_marker", "blood_cell", 'norm_adj', 'pCRC_adj'),
escape = F, booktabs = F, caption = paste("Downregulated in ", coef),
digits = c(0, 2, 2, 0, 0, 3, 2, 3, 0, 0, 0, 0)) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T,
fixed_thead = list(enabled = T)) %>%
column_spec(2, bold = T)
# Function return is to print kable, either upregulated or downregulated miRNA
return_list = list("up_mirna" = up_mirna, "down_mirna" = down_mirna,
"number_upregulated" = number_upregulated,
"number_downregulated" = number_downregulated)
return(return_list)
}
design <- as.formula(~ type)
ref <- 'tumor'
dds <- DeseqObject(design, countdata, sampleinfo, "None", "None", ref)
#
column='type'
tissue_type_A <- 'metastasis'
tissue_type_B <- 'tumor'
norm_adj_up = union(dict_sig_mirna$tissue.type_normal.liver_vs_normal.colorect_up, dict_sig_mirna$tissue.type_normal.lung_vs_normal.colorect_up)
norm_adj_down = union(dict_sig_mirna$tissue.type_normal.liver_vs_normal.colorect_down, dict_sig_mirna$tissue.type_normal.lung_vs_normal.colorect_down)
pCRC_adj_up = union(dict_sig_mirna$tissue.type_normal.liver_vs_tumor.colorect_up, dict_sig_mirna$tissue.type_normal.lung_vs_tumor.colorect_up)
pCRC_adj_down = union(dict_sig_mirna$tissue.type_normal.liver_vs_tumor.colorect_down, dict_sig_mirna$tissue.type_normal.lung_vs_tumor.colorect_down)
coef <- paste(column, tissue_type_A, 'vs', tissue_type_B, sep='_')
res <- DeseqResult(dds, column, coef, tissue_type_A, tissue_type_B,
lfc.Threshold, rpm.Threshold,
norm_adj_up,
norm_adj_down,
pCRC_adj_up,
pCRC_adj_down)
## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
##
## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
## Reference: https://doi.org/10.1093/bioinformatics/bty895
## Warning in if (!(norm_adj_up == "None")) {: the condition has length > 1 and
## only the first element will be used
## Warning in if (!(norm_adj_down == "None")) {: the condition has length > 1 and
## only the first element will be used
## Warning in if (!(pCRC_adj_up == "None")) {: the condition has length > 1 and
## only the first element will be used
## Warning in if (!(pCRC_adj_down == "None")) {: the condition has length > 1 and
## only the first element will be used
dict_sig_mirna[paste(coef, "up", sep='_')] <- list(res$up_mirna)
dict_sig_mirna[paste(coef, "down", sep='_')] <- list(res$down_mirna)
res_res <- res$res
res_dict[coef] <- res_res
## Warning in `[<-`(`*tmp*`, coef, value = new("DESeqResults", priorInfo = list(:
## implicit list embedding of S4 objects is deprecated
plotMA(res$res, alpha=0.05)

# Plot volcano plot
VolcanoPlot(res$res, coef, res$sig,
res$up_mirna, res$down_mirna,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)
## Warning in if (norm_adj_up != "None" & length(up_mirna > 0)) {: the condition
## has length > 1 and only the first element will be used
## Warning in if (norm_adj_down != "None" & length(down_mirna > 0)) {: the
## condition has length > 1 and only the first element will be used
## Warning in if (pCRC_adj_up != "None" & length(up_mirna > 0)) {: the condition
## has length > 1 and only the first element will be used
## Warning in if (pCRC_adj_down != "None" & length(down_mirna > 0)) {: the
## condition has length > 1 and only the first element will be used

#ExpressionPlot(res$res, res$rpm, coef, res$sig,
# tissue_type_A, tissue_type_B,
# res$up_mirna, res$down_mirna,
# norm_adj_up, norm_adj_down,
# pCRC_adj_up, pCRC_adj_down)
signature_mirnas <- SigList(res, dds, tissue_type_A, tissue_type_B, coef,
norm_adj_up, norm_adj_down,
pCRC_adj_up, pCRC_adj_down)
## Warning in if (norm_adj_up != "None") {: the condition has length > 1 and only
## the first element will be used
## Warning in if (pCRC_adj_up != "None") {: the condition has length > 1 and only
## the first element will be used
## Warning in if (norm_adj_down != "None") {: the condition has length > 1 and only
## the first element will be used
## Warning in if (pCRC_adj_down != "None") {: the condition has length > 1 and only
## the first element will be used
# Print list upregulated miRNA
print("control was union of lung and liver normal adjacent")
## [1] "control was union of lung and liver normal adjacent"
signature_mirnas$up_mirna
Upregulated in type_metastasis_vs_tumor
|
miRNA
|
LFC
|
lfcSE
|
RPM metastasis
|
RPM tumor
|
-log10(adj p-value)
|
cell_marker
|
blood_cell
|
norm_adj
|
pCRC_adj
|
|
Hsa-Mir-210_3p
|
1.10
|
0.13
|
625
|
291
|
14.551
|
|
|
|
|
|
Hsa-Mir-1307_5p
|
0.72
|
0.12
|
837
|
492
|
7.519
|
|
|
|
|
|
Hsa-Mir-191_5p
|
0.72
|
0.11
|
23467
|
13630
|
9.458
|
|
|
|
|
|
Hsa-Mir-8-P1b_3p
|
0.66
|
0.17
|
12715
|
7518
|
3.068
|
|
|
|
|
|
Hsa-Mir-10-P1a_5p
|
0.59
|
0.15
|
140769
|
97123
|
3.469
|
|
|
|
|
|
Hsa-Mir-150_5p
|
1.05
|
0.16
|
664
|
280
|
9.458
|
Lymphocyte
|
|
|
yes
|
|
Hsa-Mir-15-P2a_5p/P2b_5p
|
0.74
|
0.08
|
7988
|
4522
|
18.164
|
|
|
|
yes
|
|
Hsa-Mir-331_3p
|
0.70
|
0.12
|
104
|
60
|
7.519
|
|
|
|
yes
|
|
Hsa-Mir-335_5p
|
0.76
|
0.09
|
262
|
150
|
15.670
|
Retinal Epithelial Cell
|
|
yes
|
yes
|
|
Hsa-Mir-122_5p
|
1.56
|
0.31
|
1459
|
9
|
24.422
|
Hepatocyte
|
|
yes
|
yes
|
|
Hsa-Mir-126_5p
|
0.92
|
0.12
|
7434
|
3455
|
12.397
|
c(“Endothelial Cell”, “Platelet”)
|
|
yes
|
yes
|
|
Hsa-Mir-342_3p
|
1.46
|
0.13
|
472
|
145
|
27.005
|
c(“Dendritic Cell”, “Lymphocyte”, “Macrophage”)
|
|
yes
|
yes
|
|
Hsa-Mir-34-P2b_5p
|
2.61
|
0.21
|
332
|
33
|
19.564
|
|
|
yes
|
yes
|
|
Hsa-Mir-30-P1a_5p
|
0.93
|
0.13
|
7100
|
3165
|
10.616
|
|
|
yes
|
yes
|
|
Hsa-Mir-15-P2c_5p
|
0.87
|
0.12
|
537
|
257
|
10.661
|
|
|
yes
|
yes
|
|
Hsa-Mir-10-P3c_5p
|
0.82
|
0.15
|
514
|
239
|
6.775
|
|
|
yes
|
yes
|
|
Hsa-Mir-26-P1_5p/P2_5p
|
0.71
|
0.07
|
60843
|
35584
|
23.244
|
|
|
yes
|
yes
|
|
Hsa-Mir-338-P1_3p
|
0.70
|
0.16
|
169
|
98
|
4.263
|
|
|
yes
|
yes
|
|
Hsa-Mir-10-P2c_5p
|
0.70
|
0.17
|
264
|
142
|
3.839
|
|
|
yes
|
yes
|
# Number of upregulated miRNA
signature_mirnas$number_upregulated
## [1] 19
# Print list downregulated miRNA
print("control was union of lung and liver normal adjacent")
## [1] "control was union of lung and liver normal adjacent"
signature_mirnas$down_mirna
Downregulated in type_metastasis_vs_tumor
|
miRNA
|
LFC
|
lfcSE
|
RPM metastasis
|
RPM tumor
|
-log10(adj p-value)
|
cell_marker
|
blood_cell
|
norm_adj
|
pCRC_adj
|
|
Hsa-Mir-7-P1_5p/P2_5p/P3_5p
|
-0.60
|
0.18
|
112
|
199
|
2.657
|
c(“Islet Cell”, “Neural”)
|
|
|
yes
|
|
Hsa-Mir-31_5p
|
-0.68
|
0.23
|
339
|
627
|
2.235
|
|
|
|
yes
|
|
Hsa-Mir-143_3p
|
-1.08
|
0.14
|
72990
|
148431
|
13.050
|
Mesenchymal
|
|
yes
|
yes
|
|
Hsa-Mir-133-P1_3p/P2_3p/P3_3p
|
-1.80
|
0.19
|
33
|
116
|
18.097
|
c(“Skeletal Myocyte”, “Stem Cell”)
|
|
yes
|
yes
|
# Number of downregulated miRNA
signature_mirnas$number_downregulated
## [1] 4
res_dict
## $tissue.type_normal.liver_vs_normal.colorect
## log2 fold change (MAP): tissue.type normal.liver vs normal.colorect
## Wald test p-value: tissue.type normal.liver vs normal.colorect
## DataFrame with 389 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281 -0.114702438165718
## Hsa-Let-7-P1b_5p 3359.93816553835 -0.320114053507846
## Hsa-Let-7-P1c_5p 3992.69006418194 3.18371450404926
## Hsa-Let-7-P2a3_5p 52000.6252125236 0.135005279299899
## Hsa-Let-7-P2b1_5p 9751.60360980849 0.321769922951614
## ... ... ...
## Hsa-Mir-95-P2_3p 390.216333625945 -0.259423173236495
## Hsa-Mir-95-P3_5p 3.95328293251241 0.578777309350614
## Hsa-Mir-96-P1_5p 202.656949570096 -2.86706387684201
## Hsa-Mir-96-P2_5p 27209.4573430933 -2.64828835135873
## Hsa-Mir-96-P3_5p 3344.18853507094 -3.56720648489265
## lfcSE stat
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.137100418774597 -0.876702398466493
## Hsa-Let-7-P1b_5p 0.223907107455248 -1.4689025908534
## Hsa-Let-7-P1c_5p 0.279081137786442 11.1516794079232
## Hsa-Let-7-P2a3_5p 0.120493686572101 1.11711255877344
## Hsa-Let-7-P2b1_5p 0.122630483027464 2.60055162322238
## ... ... ...
## Hsa-Mir-95-P2_3p 0.19641408635223 -1.08552839444029
## Hsa-Mir-95-P3_5p 0.384387609595463 1.62826495421394
## Hsa-Mir-96-P1_5p 0.333638316705081 -7.80833454343775
## Hsa-Mir-96-P2_5p 0.243241112732279 -10.2148298811257
## Hsa-Mir-96-P3_5p 0.324276388539775 -10.0394868651728
## pvalue padj
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.380648303687109 0.543582632940632
## Hsa-Let-7-P1b_5p 0.141859211826177 0.240787346389169
## Hsa-Let-7-P1c_5p 7.02674679240312e-29 1.59961824038824e-27
## Hsa-Let-7-P2a3_5p 0.263946201391012 0.404220147427516
## Hsa-Let-7-P2b1_5p 0.0093074014267008 0.0220979408106332
## ... ... ...
## Hsa-Mir-95-P2_3p 0.277687694717835 0.421431913160009
## Hsa-Mir-95-P3_5p 0.103468717163438 0.18284197964498
## Hsa-Mir-96-P1_5p 5.79486025768406e-15 5.33954980886603e-14
## Hsa-Mir-96-P2_5p 1.7017739490561e-24 2.99357508311232e-23
## Hsa-Mir-96-P3_5p 1.02204432533755e-23 1.71970066915492e-22
##
## $tissue.type_normal.lung_vs_normal.colorect
## log2 fold change (MAP): tissue.type normal.lung vs normal.colorect
## Wald test p-value: tissue.type normal.lung vs normal.colorect
## DataFrame with 389 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281 0.159488915677012
## Hsa-Let-7-P1b_5p 3359.93816553835 0.197555867999725
## Hsa-Let-7-P1c_5p 3992.69006418194 1.96610542233694
## Hsa-Let-7-P2a3_5p 52000.6252125236 0.289175178805092
## Hsa-Let-7-P2b1_5p 9751.60360980849 1.10414120876771
## ... ... ...
## Hsa-Mir-95-P2_3p 390.216333625945 0.165907582077109
## Hsa-Mir-95-P3_5p 3.95328293251241 -0.0273303033157357
## Hsa-Mir-96-P1_5p 202.656949570096 -0.415869888949985
## Hsa-Mir-96-P2_5p 27209.4573430933 -0.107488420248239
## Hsa-Mir-96-P3_5p 3344.18853507094 -0.456955953013122
## lfcSE stat
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.171252727891764 0.886346895351987
## Hsa-Let-7-P1b_5p 0.280263432235887 0.631339921419183
## Hsa-Let-7-P1c_5p 0.350196583213482 5.61986491346816
## Hsa-Let-7-P2a3_5p 0.15044933221432 1.91622249772382
## Hsa-Let-7-P2b1_5p 0.153065517284977 7.17242652538056
## ... ... ...
## Hsa-Mir-95-P2_3p 0.243046643416424 0.843104626057063
## Hsa-Mir-95-P3_5p 0.458035265321474 0.143432621264712
## Hsa-Mir-96-P1_5p 0.388545838443054 -0.690134766070179
## Hsa-Mir-96-P2_5p 0.304517199045329 -0.0704700708063357
## Hsa-Mir-96-P3_5p 0.404724536391786 -0.772763885607241
## pvalue padj
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.375430626009251 0.530261504618906
## Hsa-Let-7-P1b_5p 0.52781828944235 0.684422033196025
## Hsa-Let-7-P1c_5p 1.91106824923059e-08 1.57358172862179e-07
## Hsa-Let-7-P2a3_5p 0.0553367808976523 0.104465044914105
## Hsa-Let-7-P2b1_5p 7.36799615073784e-13 1.05607944827242e-11
## ... ... ...
## Hsa-Mir-95-P2_3p 0.399169931746439 0.555679005704575
## Hsa-Mir-95-P3_5p 0.885948521274591 0.960397976843884
## Hsa-Mir-96-P1_5p 0.490109441851604 0.651795030916051
## Hsa-Mir-96-P2_5p 0.943819521347161 0.98187676011116
## Hsa-Mir-96-P3_5p 0.439662130177162 0.603366114817595
##
## $tissue.type_tumor.colorect_vs_normal.colorect
## log2 fold change (MAP): tissue.type tumor.colorect vs normal.colorect
## Wald test p-value: tissue.type tumor.colorect vs normal.colorect
## DataFrame with 389 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281 -0.248058505837001
## Hsa-Let-7-P1b_5p 3359.93816553835 -0.129712872859404
## Hsa-Let-7-P1c_5p 3992.69006418194 -0.0894387113372064
## Hsa-Let-7-P2a3_5p 52000.6252125236 0.161274363696562
## Hsa-Let-7-P2b1_5p 9751.60360980849 -0.129885530327206
## ... ... ...
## Hsa-Mir-95-P2_3p 390.216333625945 1.16639047991208
## Hsa-Mir-95-P3_5p 3.95328293251241 0.128041251542255
## Hsa-Mir-96-P1_5p 202.656949570096 1.99605999628903
## Hsa-Mir-96-P2_5p 27209.4573430933 1.8371528804308
## Hsa-Mir-96-P3_5p 3344.18853507094 1.5305097819346
## lfcSE stat
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.09974589771869 -2.50880006172293
## Hsa-Let-7-P1b_5p 0.160828091638605 -0.872616006654668
## Hsa-Let-7-P1c_5p 0.198255228124062 -0.14744126618236
## Hsa-Let-7-P2a3_5p 0.0878077644317027 1.81863066456019
## Hsa-Let-7-P2b1_5p 0.0893412951279261 -1.43703312496472
## ... ... ...
## Hsa-Mir-95-P2_3p 0.140554605668826 8.28848119000958
## Hsa-Mir-95-P3_5p 0.264177931179274 0.737283525185868
## Hsa-Mir-96-P1_5p 0.217577914511228 8.93516022919441
## Hsa-Mir-96-P2_5p 0.173955103251303 10.4458859814953
## Hsa-Mir-96-P3_5p 0.226074853962932 6.65702796429079
## pvalue padj
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.0121142030924821 0.0250889779893481
## Hsa-Let-7-P1b_5p 0.38287241295549 0.480492200364671
## Hsa-Let-7-P1c_5p 0.882783735719796 0.926800653290313
## Hsa-Let-7-P2a3_5p 0.0689677962587844 0.122143900850838
## Hsa-Let-7-P2b1_5p 0.150708581526998 0.225175837320617
## ... ... ...
## Hsa-Mir-95-P2_3p 1.1470369823237e-16 1.40234521387317e-15
## Hsa-Mir-95-P3_5p 0.460949948804283 0.561736432787213
## Hsa-Mir-96-P1_5p 4.06588568948011e-19 8.56094820173867e-18
## Hsa-Mir-96-P2_5p 1.53019828709604e-25 6.44383500899331e-24
## Hsa-Mir-96-P3_5p 2.79420040427442e-11 1.96111472818519e-10
##
## $tissue.type_normal.liver_vs_tumor.colorect
## log2 fold change (MAP): tissue.type normal.liver vs tumor.colorect
## Wald test p-value: tissue.type normal.liver vs tumor.colorect
## DataFrame with 389 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281 0.134516855928531
## Hsa-Let-7-P1b_5p 3359.93816553835 -0.18431192252938
## Hsa-Let-7-P1c_5p 3992.69006418194 3.24635633273589
## Hsa-Let-7-P2a3_5p 52000.6252125236 -0.0245108495144203
## Hsa-Let-7-P2b1_5p 9751.60360980849 0.451258084717704
## ... ... ...
## Hsa-Mir-95-P2_3p 390.216333625945 -1.41840794320829
## Hsa-Mir-95-P3_5p 3.95328293251241 0.414413322947411
## Hsa-Mir-96-P1_5p 202.656949570096 -4.81237877419974
## Hsa-Mir-96-P2_5p 27209.4573430933 -4.46595236546167
## Hsa-Mir-96-P3_5p 3344.18853507094 -5.07557947770261
## lfcSE stat
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.11131421654252 1.19461982343763
## Hsa-Let-7-P1b_5p 0.184409102642062 -1.03024741017946
## Hsa-Let-7-P1c_5p 0.232675048404373 13.987286383856
## Hsa-Let-7-P2a3_5p 0.0976442188709745 -0.267757085486139
## Hsa-Let-7-P2b1_5p 0.0994097267910815 4.53746602977892
## ... ... ...
## Hsa-Mir-95-P2_3p 0.161409886777642 -8.797157721989
## Hsa-Mir-95-P3_5p 0.332664401974054 1.34224159185026
## Hsa-Mir-96-P1_5p 0.288868128705996 -16.6454924674176
## Hsa-Mir-96-P2_5p 0.201189454623787 -22.1766896561084
## Hsa-Mir-96-P3_5p 0.274372198803725 -18.4172382155403
## pvalue padj
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.232235600538294 0.301594555061476
## Hsa-Let-7-P1b_5p 0.302893879225991 0.381823880327226
## Hsa-Let-7-P1c_5p 1.86389083593418e-44 3.13619892828925e-43
## Hsa-Let-7-P2a3_5p 0.788886305673403 0.867326705385247
## Hsa-Let-7-P2b1_5p 5.69341978961745e-06 1.4887523368797e-05
## ... ... ...
## Hsa-Mir-95-P2_3p 1.40325015866435e-18 7.75796873433006e-18
## Hsa-Mir-95-P3_5p 0.179517674775297 0.240392180408443
## Hsa-Mir-96-P1_5p 3.26270971024334e-62 7.89167911165108e-61
## Hsa-Mir-96-P2_5p 5.76691295122432e-109 4.46359062424763e-107
## Hsa-Mir-96-P3_5p 9.55562651928938e-76 2.84463650997307e-74
##
## $tissue.type_normal.lung_vs_tumor.colorect
## log2 fold change (MAP): tissue.type normal.lung vs tumor.colorect
## Wald test p-value: tissue.type normal.lung vs tumor.colorect
## DataFrame with 389 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281 0.408125846033093
## Hsa-Let-7-P1b_5p 3359.93816553835 0.332599900506941
## Hsa-Let-7-P1c_5p 3992.69006418194 2.020544361604
## Hsa-Let-7-P2a3_5p 52000.6252125236 0.129817388788356
## Hsa-Let-7-P2b1_5p 9751.60360980849 1.23298675588116
## ... ... ...
## Hsa-Mir-95-P2_3p 390.216333625945 -0.987860472334413
## Hsa-Mir-95-P3_5p 3.95328293251241 -0.191008452152824
## Hsa-Mir-96-P1_5p 202.656949570096 -2.3341261742075
## Hsa-Mir-96-P2_5p 27209.4573430933 -1.90878481829015
## Hsa-Mir-96-P3_5p 3344.18853507094 -1.93676271625054
## lfcSE stat
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.151440987364688 2.68337077689517
## Hsa-Let-7-P1b_5p 0.249992618790543 1.30066611269162
## Hsa-Let-7-P1c_5p 0.314717268191971 6.48763117983787
## Hsa-Let-7-P2a3_5p 0.132884439212234 0.963563940574538
## Hsa-Let-7-P2b1_5p 0.135208009802693 9.11432613077411
## ... ... ...
## Hsa-Mir-95-P2_3p 0.215830901283507 -4.59605232940141
## Hsa-Mir-95-P3_5p 0.415244927482672 -0.364000003055949
## Hsa-Mir-96-P1_5p 0.350565916495824 -6.75367079090678
## Hsa-Mir-96-P2_5p 0.272289955724401 -7.05756424076837
## Hsa-Mir-96-P3_5p 0.366207615281681 -5.32493458906083
## pvalue padj
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.00728841357441722 0.0138946603610811
## Hsa-Let-7-P1b_5p 0.193372766419832 0.281397733924404
## Hsa-Let-7-P1c_5p 8.71964116059556e-11 4.62260428650751e-10
## Hsa-Let-7-P2a3_5p 0.335264592721268 0.444340401997023
## Hsa-Let-7-P2b1_5p 7.91608806736923e-20 9.88234220023191e-19
## ... ... ...
## Hsa-Mir-95-P2_3p 4.3057060677156e-06 1.35472215301296e-05
## Hsa-Mir-95-P3_5p 0.715858007098838 0.805340257986192
## Hsa-Mir-96-P1_5p 1.44150633672609e-11 8.45246897443934e-11
## Hsa-Mir-96-P2_5p 1.69446438026879e-12 1.11145375451529e-11
## Hsa-Mir-96-P3_5p 1.00989374164518e-07 3.947766444613e-07
##
## $tissue.type_metastasis.liver_vs_tumor.colorect
## log2 fold change (MAP): tissue.type metastasis.liver vs tumor.colorect
## Wald test p-value: tissue.type metastasis.liver vs tumor.colorect
## DataFrame with 389 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281 -0.0291372578148132
## Hsa-Let-7-P1b_5p 3359.93816553835 -0.0384102612949004
## Hsa-Let-7-P1c_5p 3992.69006418194 0.165919575225585
## Hsa-Let-7-P2a3_5p 52000.6252125236 -0.0667790393731562
## Hsa-Let-7-P2b1_5p 9751.60360980849 -0.022055090655077
## ... ... ...
## Hsa-Mir-95-P2_3p 390.216333625945 0.197986570574988
## Hsa-Mir-95-P3_5p 3.95328293251241 0.87517582162084
## Hsa-Mir-96-P1_5p 202.656949570096 -0.153456519278348
## Hsa-Mir-96-P2_5p 27209.4573430933 0.0668521603991884
## Hsa-Mir-96-P3_5p 3344.18853507094 0.480378406060166
## lfcSE stat
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.087811822186451 -0.34524409879388
## Hsa-Let-7-P1b_5p 0.143352612343951 -0.309464165623311
## Hsa-Let-7-P1c_5p 0.178645206267475 1.08002602868534
## Hsa-Let-7-P2a3_5p 0.0771691545429361 -0.884124420143461
## Hsa-Let-7-P2b1_5p 0.0785334182092049 -0.274463101822189
## ... ... ...
## Hsa-Mir-95-P2_3p 0.123880822993862 1.53530515265106
## Hsa-Mir-95-P3_5p 0.226325334424462 3.88027612923505
## Hsa-Mir-96-P1_5p 0.196070125088779 -0.994964941991442
## Hsa-Mir-96-P2_5p 0.155589993744074 0.303386567615509
## Hsa-Mir-96-P3_5p 0.205702483629426 2.17135483299889
## pvalue padj
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.729910868456285 0.886990854282231
## Hsa-Let-7-P1b_5p 0.756968466884636 0.898589554206931
## Hsa-Let-7-P1c_5p 0.280130589584733 0.477579463300844
## Hsa-Let-7-P2a3_5p 0.376629052036082 0.589523977412743
## Hsa-Let-7-P2b1_5p 0.783728755707429 0.90746054828939
## ... ... ...
## Hsa-Mir-95-P2_3p 0.124708889065693 0.283896118049548
## Hsa-Mir-95-P3_5p 0.000104337942960983 0.000776515075498082
## Hsa-Mir-96-P1_5p 0.31975331550125 0.522757414533685
## Hsa-Mir-96-P2_5p 0.761595281084944 0.898589554206931
## Hsa-Mir-96-P3_5p 0.0299043603592752 0.0972519954541135
##
## $tissue.type_metastasis.lung_vs_tumor.colorect
## log2 fold change (MAP): tissue.type metastasis.lung vs tumor.colorect
## Wald test p-value: tissue.type metastasis.lung vs tumor.colorect
## DataFrame with 389 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281 -0.371287975266059
## Hsa-Let-7-P1b_5p 3359.93816553835 -0.86478570992646
## Hsa-Let-7-P1c_5p 3992.69006418194 0.135121670461325
## Hsa-Let-7-P2a3_5p 52000.6252125236 -0.362077135000897
## Hsa-Let-7-P2b1_5p 9751.60360980849 0.0403928595342307
## ... ... ...
## Hsa-Mir-95-P2_3p 390.216333625945 0.244457301347207
## Hsa-Mir-95-P3_5p 3.95328293251241 0.770986123093289
## Hsa-Mir-96-P1_5p 202.656949570096 -0.746540861417062
## Hsa-Mir-96-P2_5p 27209.4573430933 -0.78147625945163
## Hsa-Mir-96-P3_5p 3344.18853507094 -0.326709592212615
## lfcSE stat
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.0959152266130106 -3.87096089411713
## Hsa-Let-7-P1b_5p 0.156536850438983 -5.51447766923507
## Hsa-Let-7-P1c_5p 0.194969507259272 0.835611129624883
## Hsa-Let-7-P2a3_5p 0.0842935080279992 -4.30344211748187
## Hsa-Let-7-P2b1_5p 0.0857694713451329 0.474626440937984
## ... ... ...
## Hsa-Mir-95-P2_3p 0.135123404458061 1.75056733087186
## Hsa-Mir-95-P3_5p 0.24193629758336 3.22815141458786
## Hsa-Mir-96-P1_5p 0.213973544808363 -3.63856405996494
## Hsa-Mir-96-P2_5p 0.169868922201866 -4.66162915975394
## Hsa-Mir-96-P3_5p 0.224439059665663 -1.53101854305705
## pvalue padj
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.0001084071837261 0.00040731631166991
## Hsa-Let-7-P1b_5p 3.49817317913999e-08 2.5543264534475e-07
## Hsa-Let-7-P1c_5p 0.403373705499327 0.532783699755084
## Hsa-Let-7-P2a3_5p 1.68164797190917e-05 7.65644429563353e-05
## Hsa-Let-7-P2b1_5p 0.635053256632943 0.757086098142951
## ... ... ...
## Hsa-Mir-95-P2_3p 0.0800204667405637 0.14746628870761
## Hsa-Mir-95-P3_5p 0.00124593006501447 0.00359832041164626
## Hsa-Mir-96-P1_5p 0.000274162436246517 0.00098241539655002
## Hsa-Mir-96-P2_5p 3.13716090303122e-06 1.57672892139361e-05
## Hsa-Mir-96-P3_5p 0.125764809854213 0.211612962667741
##
## $tissue.type_metastasis.pc_vs_tumor.colorect
## log2 fold change (MAP): tissue.type metastasis.pc vs tumor.colorect
## Wald test p-value: tissue.type metastasis.pc vs tumor.colorect
## DataFrame with 389 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281 -0.194926960102631
## Hsa-Let-7-P1b_5p 3359.93816553835 -0.170006997664929
## Hsa-Let-7-P1c_5p 3992.69006418194 0.950788049188073
## Hsa-Let-7-P2a3_5p 52000.6252125236 -0.235739655653004
## Hsa-Let-7-P2b1_5p 9751.60360980849 -0.0275610759199226
## ... ... ...
## Hsa-Mir-95-P2_3p 390.216333625945 -0.391736823339089
## Hsa-Mir-95-P3_5p 3.95328293251241 0.193480385452117
## Hsa-Mir-96-P1_5p 202.656949570096 -0.537251798926674
## Hsa-Mir-96-P2_5p 27209.4573430933 -0.144468039225267
## Hsa-Mir-96-P3_5p 3344.18853507094 -0.149480720489416
## lfcSE stat
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.0919497346955033 -2.12106919715872
## Hsa-Let-7-P1b_5p 0.146429745186075 -1.18446872666545
## Hsa-Let-7-P1c_5p 0.178397588170818 5.34338828103208
## Hsa-Let-7-P2a3_5p 0.08107727951498 -2.91425069775732
## Hsa-Let-7-P2b1_5p 0.0824764385946126 -0.32740564020296
## ... ... ...
## Hsa-Mir-95-P2_3p 0.128019210385648 -3.07056591636714
## Hsa-Mir-95-P3_5p 0.222349699850271 0.967689700538171
## Hsa-Mir-96-P1_5p 0.193381569174697 -2.9161157752392
## Hsa-Mir-96-P2_5p 0.157787812752101 -1.01238717153534
## Hsa-Mir-96-P3_5p 0.201177138993338 -0.825123443737066
## pvalue padj
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.0339159796632756 0.0901196031052751
## Hsa-Let-7-P1b_5p 0.236227568744681 0.370368857833221
## Hsa-Let-7-P1c_5p 9.12250626572605e-08 1.40060447984391e-06
## Hsa-Let-7-P2a3_5p 0.00356543456685069 0.0141511415829511
## Hsa-Let-7-P2b1_5p 0.743361101075885 0.848252544785979
## ... ... ...
## Hsa-Mir-95-P2_3p 0.00213653519975517 0.009575796316975
## Hsa-Mir-95-P3_5p 0.333199363429196 0.48229635484693
## Hsa-Mir-96-P1_5p 0.00354418957989829 0.0141511415829511
## Hsa-Mir-96-P2_5p 0.311352969649893 0.46861091937278
## Hsa-Mir-96-P3_5p 0.409301511166841 0.561003252050181
##
## $type_metastasis_vs_tumor
## log2 fold change (MAP): type metastasis vs tumor
## Wald test p-value: type metastasis vs tumor
## DataFrame with 389 rows and 6 columns
## baseMean log2FoldChange
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 89174.7542403281 -0.180701783114052
## Hsa-Let-7-P1b_5p 3359.93816553835 -0.294623990307531
## Hsa-Let-7-P1c_5p 3992.69006418194 0.48926666782258
## Hsa-Let-7-P2a3_5p 52000.6252125236 -0.206937259890988
## Hsa-Let-7-P2b1_5p 9751.60360980849 -0.00482780603867569
## ... ... ...
## Hsa-Mir-95-P2_3p 390.216333625945 0.0426916660211096
## Hsa-Mir-95-P3_5p 3.95328293251241 0.678957110901101
## Hsa-Mir-96-P1_5p 202.656949570096 -0.464482054965902
## Hsa-Mir-96-P2_5p 27209.4573430933 -0.221824399033929
## Hsa-Mir-96-P3_5p 3344.18853507094 0.0672687256455847
## lfcSE stat
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.0639958634974693 -2.81887235438572
## Hsa-Let-7-P1b_5p 0.106490720796663 -2.76160115635425
## Hsa-Let-7-P1c_5p 0.149925843445819 3.33812788609782
## Hsa-Let-7-P2a3_5p 0.0563710904824311 -3.67052297626806
## Hsa-Let-7-P2b1_5p 0.0617282834747614 -0.070874324259544
## ... ... ...
## Hsa-Mir-95-P2_3p 0.0918217873522948 0.437722915855047
## Hsa-Mir-95-P3_5p 0.170411797990612 3.95560185906774
## Hsa-Mir-96-P1_5p 0.152114644217898 -3.14528253438741
## Hsa-Mir-96-P2_5p 0.12892939045268 -1.79236001772164
## Hsa-Mir-96-P3_5p 0.164329004650402 0.314884459867911
## pvalue padj
## <numeric> <numeric>
## Hsa-Let-7-P1a_5p/P2a1_5p/P2a2_5p 0.00481926786262815 0.0172690431744175
## Hsa-Let-7-P1b_5p 0.00575186955495521 0.0200538154753844
## Hsa-Let-7-P1c_5p 0.00084344919760993 0.00370925953948912
## Hsa-Let-7-P2a3_5p 0.000242054709304128 0.00121656068182724
## Hsa-Let-7-P2b1_5p 0.943497778247006 0.97368970715091
## ... ... ...
## Hsa-Mir-95-P2_3p 0.661587155561431 0.823261187145574
## Hsa-Mir-95-P3_5p 7.63422117073344e-05 0.000440961730309529
## Hsa-Mir-96-P1_5p 0.00165926501866658 0.00690468346477385
## Hsa-Mir-96-P2_5p 0.0730753150258787 0.17139482978797
## Hsa-Mir-96-P3_5p 0.752849381089381 0.86454810231926